USEF-TSE: Universal Speaker Embedding Free Target Speaker Extraction
Bang Zeng Ming Li





        











USEF-TSE: Universal Speaker Embedding Free Target Speaker Extraction
    Bang Zeng, Ming Li
    July 2024
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§ ABSTRACT 
Target speaker extraction aims to isolate the voice of a specific speaker from mixed speech. Traditionally, this process has relied on extracting a speaker embedding from a reference speech, necessitating a speaker recognition model. However, identifying an appropriate speaker recognition model can be challenging, and using the target speaker embedding as reference information may not be optimal for target speaker extraction tasks. This paper introduces a Universal Speaker Embedding-Free Target Speaker Extraction (USEF-TSE) framework that operates without relying on speaker embeddings. USEF-TSE utilizes a multi-head cross-attention mechanism as a frame-level target speaker feature extractor. This innovative approach allows mainstream speaker extraction solutions to bypass the dependency on speaker recognition models and to fully leverage the information available in the enrollment speech, including speaker characteristics and contextual details. Additionally, USEF-TSE can seamlessly integrate with any time-domain or time-frequency domain speech separation model to achieve effective speaker extraction. Experimental results show that our proposed method achieves state-of-the-art (SOTA) performance in terms of Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) on the WSJ0-2mix, WHAM!, and WHAMR! datasets, which are standard benchmarks for monaural anechoic, noisy and noisy-reverberant two-speaker speech separation and speaker extraction.



Target speaker extraction, speaker recognition model, speaker embedding free.




§ INTRODUCTION

Humans have the remarkable ability to selectively focus on a specific speech signal in a noisy environment, a skill often referred to as the cocktail party problem <cit.>. Much research has been devoted to enabling machines to replicate this ability. Speech separation has emerged as a practical solution to address this challenge. It plays a vital role as a preliminary step for various speech signal processing technologies, including speech recognition, speaker recognition, and speaker diarization <cit.>.

Traditional speech separation algorithms, such as non-negative matrix factorization (NMF) and computational auditory scene analysis (CASA) <cit.>, typically use spectro-temporal masking to isolate each speaker’s voice from mixed speech. While these methods have significantly advanced speech separation technology, they often struggle with handling nonlinear or non-stationary signals and exhibit limited generalization. With the rapid advancement of deep learning, new methods that employ deep neural networks to estimate mask matrices have emerged, offering improved performance and adaptability.

Deep neural network-based speech separation algorithms, such as Deep Clustering (DC) <cit.>, Deep Attractor Network (DANet) <cit.>, and Permutation Invariant Training (PIT) <cit.>, have markedly enhanced separation performance. Moreover, DC and PIT have effectively addressed the label permutation problem. However, the time-domain audio separation networks (TasNet) <cit.> highlight a significant limitation of time-frequency domain speech separation algorithms: their inability to accurately reconstruct the phase of clean speech. TasNet addresses this by using convolutions and deconvolutions in place of the short-time Fourier transform (STFT) and inverse STFT (iSTFT) for modeling and reconstructing target speech. Consequently, time-domain speech separation approaches <cit.> have become widely adopted. Various time-domain methods, such as Dual-Path RNN (DPRNN) <cit.>, SepFormer <cit.> and Mossformer2 <cit.>, have significantly improved separation performance, with Mossformer2 <cit.> achieving state-of-the-art (SOTA) results. Recently, the introduction of TF-GridNet <cit.> has led to renewed excellence in time-frequency domain speech separation <cit.>, rekindling interest in this area. Despite these advances, many speech separation methods still require prior knowledge of the number of speakers in the mixed speech. This prior knowledge is not always available in real-world applications, which poses challenges for applying these separation solutions in practical scenarios. 

Recently, target speaker extraction <cit.> models have emerged as essential tools for addressing the limitations of traditional speech separation methods, especially in scenarios where the number of speakers in the audio mixture is unknown. By leveraging reference speech, these models can effectively isolate the target speaker’s voice from complex audio mixtures, making them highly applicable in real-world situations. This innovative approach not only improves the practicality of speech separation technology but also expands its applicability in diverse and dynamic acoustic environments. Figure <ref> depicts a typical target speaker extraction framework. In the time-frequency (T-F) domain, the encoder and decoder represent the STFT and iSTFT operations, respectively. In the time domain, these components correspond to convolution and deconvolution operations. Traditional target speaker extraction models rely on a speaker embedding extractor to obtain the target speaker’s embedding from the reference speech. This extractor, such as a ResNet model <cit.>, can be pre-trained or jointly trained with the separator through multi-task learning. Building upon the framework illustrated in Figure <ref>, various techniques have been developed to enhance speaker extraction performance. However, these models aim to maximize speaker recognition performance by training the embedding extractor. Consequently, the speaker embedding captures only the target speaker’s information, which means that traditional speaker extraction models may not fully utilize the information available in the reference speech. As a result, relying on speaker embeddings for the speaker extraction task may not be optimal. 


In our previous work, we are the first to propose a time-domain speaker embedding-free target speaker extraction model (SEF-Net) <cit.>. It offers a viable new solution for isolating a target speaker’s voice without relying on speaker embeddings. In SEF-Net <cit.>, both the mixed speech and reference speech are processed using twin weights-sharing conformer <cit.> encoders. SEF-Net <cit.> then employs cross multi-head attention within the transformer <cit.> decoder to implicitly leverage the speaker information in the reference speech’s conformer encoder outputs. SEF-Net <cit.> has achieved comparable performance to typical target speaker extraction models, highlighting the effectiveness of the speaker embedding-free framework for target speaker extraction tasks.




While SEF-Net <cit.> demonstrates significant potential in target speaker extraction, it does have some limitations. Firstly, SEF-Net <cit.> requires either repetition or zero-padding to align the length of the reference speech with the mixed speech, which poses practical challenges. Secondly, SEF-Net <cit.> utilizes a multi-head cross-attention mechanism within both intra-block and inter-block modules to facilitate interaction between the features of mixed and reference speech. However, this approach does not fully exploit global information, potentially constraining the model’s performance improvements. Thirdly, SEF-Net <cit.> has yet to be tested on data with noise and reverberation, leaving its effectiveness in natural, noisy environments unverified. Lastly, unlike speaker embedding-based target speaker extraction methods, SEF-Net <cit.> is not a universal framework. It cannot be easily integrated with arbitrary time-domain or time-frequency domain speech separation networks for target speaker extraction tasks.

To address the aforementioned issues, this paper proposes a Universal Speaker Embedding-Free Target Speaker Extraction (USEF-TSE) framework for monaural target speaker extraction based on SEF-Net <cit.>. USEF-TSE employs a unified encoder to process both the reference and mixed speech. The encoding of the mixed speech is utilized to query the encoding of the reference speech through a cross multi-head attention (CMHA) module. The output of the CMHA module is a frame-level feature that maintains the same length as the encoded features of the mixed speech. This frame-level feature represents the target speaker’s attributes and is fused with the encoding of the mixed speech. The resulting fused feature is then fed into a separator to model the target speaker’s voice components.



This paper builds upon and extends our previous work on speaker embedding-free approaches for monaural target speaker extraction. The key contributions of this article are summarized as follows:


  * Improved: We address the limitation of SEF-Net, which requires the lengths of the reference and mixed speech to be the same. Additionally, we have altered the interaction position of the reference and mixed speech, enhancing the model’s ability to learn global information more effectively. Experimental results demonstrate that these modifications significantly improve the model’s performance.

  * Universal: We introduce USEF-Net, a universal speaker embedding-free target speaker extraction framework. USEF-Net can seamlessly integrate with any deep neural network-based time-domain or time-frequency domain speech separation model for target speaker extraction. To validate the effectiveness of USEF-Net, we propose time and T-F domain target speaker extraction models, namely USEF-SepFormer and USEF-TFGridNet. Experimental results show that our proposed methods achieve SOTA performance on the WSJ0-2mix <cit.> datasets.


  * Robust: To assess the real-world performance of the USEF-TSE framework, we evaluated it on the <cit.> and WHAMR! <cit.> datasets, achieving SOTA results.




§ RELATED WORK



 §.§ Target Speaker Extraction using speaker embedding

Target speaker extraction technology has advanced significantly, with methods generally categorized into time and T-F domain approaches.T-F domain methods typically involve applying the STFT to mixed speech signals and then separating the speech by estimating each speaker’s T-F masks. For instance, early approaches such as VoiceFilter <cit.> and SpeakerBeam <cit.> employed deep learning techniques for T-F mask estimation. However, a key challenge with these methods is the phase estimation problem, as accurately recovering phase information from the magnitude spectrum of the STFT is difficult.

With the recognition of deep learning's advantages in time-domain signal processing, time-domain schemes have gained significant attention. These methods operate directly on time series, avoiding the phase estimation challenges associated with the STFT. For example, models such as TasNet <cit.> and Conv-TasNet <cit.> have demonstrated effectiveness in speech separation tasks by using learnable 1-D convolutional neural networks to extract features directly from speech signals. SpEx <cit.> extended this approach to target speaker extraction, improving accuracy and speech quality through multi-scale embedding coefficients and a multi-task learning framework. SpEx+ <cit.> advanced this further by proposing a comprehensive time-domain speaker extraction solution. By sharing weights between two identical speech encoder networks, SpEx+ addressed the feature space mismatch in SpEx <cit.>. SpEx_pc <cit.> enhanced the utilization of speaker information by integrating speaker embeddings with speech features to predict the target speaker’s mask, exploring how to better leverage speaker information within deep network structures for improved extraction performance. X-SepFormer <cit.> developed an end-to-end target speaker extraction model that builds upon X-vectors <cit.> and SepFormer <cit.>. X-SepFormer introduced two novel loss schemes that redefine the training objective, focusing on reconstruction performance improvement metrics at the trim segment level and using distribution information related to these metrics. This approach helps the network concentrate on segments with speaker confusion (SC) <cit.> issues, enhancing system performance while reducing computational costs. X-TF-GridNet <cit.> employs U^2-Net <cit.> and TF-GridNet <cit.> as the speaker embedding extractor and separator backbone, respectively. Additionally, X-TF-GridNet utilizes adaptive speaker embedding fusion to effectively separate and enhance the target speaker's speech in complex acoustic environments.

Despite its successes, this approach may not fully leverage the contextual information available during registration.





 §.§ Speaker Embedding Free Target Speaker Extraction



Traditional target speaker extraction methods depend on speaker embeddings, which are fixed-dimensional representations derived from reference speech. Although speaker embeddings capture speaker characteristics effectively, these methods may not fully utilize all the information in the reference speech. Crucial content details, such as local dynamics and temporal structures, are essential for guiding more effective speaker extraction.

Recent advancements have explored embedding-free approaches  <cit.> that leverage frame-level acoustic features from the reference speech instead of relying on speaker embeddings. This shift addresses the above limitations of traditional methods. These methods do not require a pre-trained speaker embedding extractor or a speaker recognition loss function, focusing instead on directly processing the audio signals to extract the target speaker. SEF-Net <cit.> employs a dual-path structure with conformer encoders and a transformer decoder, using cross-multi-head attention to implicitly utilize the speaker information embedded in the reference speech’s conformer encoding outputs. This model achieves performance comparable to other target speaker extraction methods without relying on speaker embeddings, offering a novel solution to the embedding mismatch problem. The VE-VE Framework <cit.> introduces a Voice Extractor-Voice Extractor framework designed to handle target speaker extraction with ultra-short reference speech. This framework utilizes an RNN-based voice extractor, which relies on RNN states rather than speaker embeddings to capture speaker characteristics. This method addresses the feature fusion problem and effectively supports reference speech, though it is limited to RNN-based extraction networks. With the success of TF-GridNet <cit.>, there has been a gradual transition from time-domain to T-F domain approaches in target speaker extraction. Inspired by SEF-Net <cit.>, models such as SMMA-Net <cit.> and CIENet <cit.> employ attention mechanisms to interact with the T-F representations of both the reference and mixed signals. These models aim to achieve consistent T-F representations that guide more effective extraction by leveraging contextual information directly in the TF domain.

Although these approaches have yielded relatively good results, they have not established a general framework for speaker embedding-free target speaker extraction. This paper extends our previous work on speaker embedding-free target speaker extraction <cit.> to develop a high-performance, universal, and robust framework for target speaker extraction.





§ UNIVERSAL SPEAKER EMBEDDING FREE TARGET SPEAKER EXTRACTION

In this section, we will first review our previous work, SEF-Net <cit.>. Next, we will introduce the improvements and details of the USEF-TSE framework compared to SEF-Net <cit.>. To validate the universality and effectiveness of USEF-TSE, we will use SepFormer <cit.> and TF-GridNet <cit.> as backbones for the separator. This will involve constructing time-domain and T-F domain target speaker extraction models, named USEF-SepFormer and USEF-TFGridNet, respectively.














 §.§ Recap: SEF-Net


SEF-Net <cit.> is a masking-based time-domain target speaker extraction network that does not use speaker embeddings. The architecture of SEF-Net <cit.> is illustrated in Figure <ref>. SEF-Net employs two weight-sharing convolutional encoders to process the mixed speech m and the reference speech r, obtaining STFT-like representations E_m∈ℝ^N × L_1 and E_r∈ℝ^N × L_2. Here, m∈ℝ^1 × T_1 and r∈ℝ^1 × T_2 represent the mixed speech and reference speech, respectively. N denotes the feature dimension, while L_1, L_2 represent the number of frames. In the segmentation stage, E_m and Er are divided into 3-D features S_m∈ℝ^N × K × S_1 and S_r∈ℝ^N × K × S_2. Here, K is the length of chunks, and S_1, S_2 denote the number of chunks. The entire process  of the mixed speech can be formulated as follows:


    E_m = Enc(m)




    S_m = Seg(E_m)


where Enc(·) denotes the convolutional encoder and Seg(·) represents the segmentation operation.


The diagram of the masking network is shown in the center of Figure <ref>. The masking network adopts a dual-path structure similar to SepFormer <cit.>, which consists of an intra-module and an inter-module. The details of the intra-module are depicted on the right side of Figure <ref>. This module includes two parallel conformer encoders and a transformer decoder. The shared-weight intra-conformer encoders handle intra-chunk processing for S_m and S_r. The conformer encoding of S_r is then used to query the conformer encoding of S_m in the transformer decoder. These conformer encodings are denoted as C_m and C_r. In this setup, the cross multi-head attention layer within the transformer decoder functions as a feature fusion module:


    C_m = CE(S_m),C_r = CE(S_r)










    D_intra = TD(q=C_r; k,v=C_m)


where CE(·) and TD(·) represent the conformer encoder and transformer decoder, respectively. The output of the intra-module is denoted as D_intra. The inter-module shares the same components and structure as the intra-module, performing inter-chunk processing on the permuted D_intra. The operations in the inter-conformer are the same as the intra-module. D_inter denotes the output of the inter-module. After processing through the masking network, SEF-Net <cit.> applies an overlap-add operation to D_inter, transforming it back into a 2-D feature. Finally, the decoder derives the estimation of the target speaker.



 §.§ Architecture


Although SEF-Net <cit.> achieves performance on par with other target speaker extraction methods, it has certain limitations. In SEF-Net <cit.>, the transformer decoder is tasked with fusing mixed and reference speech features and extracting the target speaker's speech. This dual responsibility may constrain the model’s separation performance. Furthermore, in Equation <ref>, the lengths of C_m and C_r must be identical, requiring that the mixed speech length T_1 match the reference speech length T_2. This requirement significantly hampers the model’s practical applicability.

To address the aforementioned issues, USEF-TSE improves and extends SEF-Net <cit.>. Figure <ref> illustrates the overall flowchart of USEF-TSE, a universal framework applicable to target speaker extraction in both the time and T-F domains. The critical components of USEF-TSE include an Encoder, CMHA module, fusion module, separator, and decoder.



  §.§.§ Encoder

USEF-TSE uses the same encoder for both reference and mixed speech. This encoder can be STFT or a one-dimensional convolution, depending on whether the model works in the time or T-F domain.






  §.§.§ CMHA module

Unlike SEF-Net <cit.>, which combines feature fusion and target speaker extraction within the transformer decoder, USEF-TSE separates these tasks to improve performance. The encoder outputs of m and r are fed into the CMHA module, where a cross multi-head attention mechanism is applied to extract frame-level features of the target speaker:


    E_spk = CMHA(q=E_m; k,v=E_r)



where E_m∈ℝ^N × L_1 and E_r∈ℝ^N × L_2 represent the encoder outputs of the mixed speech and reference speech, respectively. The CMHA operation is denoted as CMHA·, and E_spk∈ℝ^N × L_1 is the output of the CMHA module. Unlike SEF-Net <cit.>, the CMHA module in USEF-TSE uses mixed speech encoding as the query. This approach produces a frame-level feature with the same length as E_m, allowing the mixed and reference speech lengths to differ in the USEF-TSE framework.



  §.§.§ Fusion module


USEF-TSE uses E_spk as the reference feature for the target speaker and applies a feature fusion module to combine E_spk with E_m:


    E_f = F(E_m, E_spk)



where E_f denotes the output of the fusion module. The feature fusion, represented by F(·), can be implemented using methods like feature concatenation or feature-wise linear modulation (FiLM) <cit.>.



  §.§.§ Separator

E_f is fed into the separator to extract the target speaker’s components. The separator can be any deep neural network (DNN) based speech separation backbone, such as DPRNN <cit.>, SepFormer <cit.>, or TF-GridNet <cit.>. In masking-based approaches, the separator’s output is element-wise multiplied with E_m before being passed to the decoder. In mapping-based approaches, the separator’s output is directly sent to the decoder.



  §.§.§ Decoder

Corresponding to the encoder, the decoder can be either iSTFT or a one-dimensional deconvolution:


    E_o = S(E_f)




    est = Dec(M(E_o))


where E_o ∈ ℝ^N × L_1 denotes the output of the separator. M(·) refers to the masking or mapping operation. S(·) and Dec(·) represent the separator and decoder, respectively. est ∈ ℝ^1 × T_1 denotes the final estimation of the target speaker.



 §.§ Time and TF Domain USEF-TSE

Building on the USEF-TSE framework, we propose two target speaker extraction models: USEF-SepFormer and USEF-TFGridNet. These models employ the SepFormer <cit.> and TF-GridNet <cit.> backbone networks as separators the USEF-TSE architecture . Accordingly, USEF-SepFormer operates in the time domain, while USEF-TFGridNet operates in the T-F domain for target speaker extraction.


  §.§.§ USEF-SepFormer

The diagram of the USEF-SepFormer is shown in the Figure <ref>. In this model,  two weight sharing encoders are used to process the mixed and reference speech:


    E_m = ReLU(Conv1d(m))




    E_r = ReLU(Conv1d(f))


where E_m ∈ ℝ^B × N × L_1 and E_r ∈ ℝ^B × N × L_2 represent the encoded outputs of the mixed and reference speech, respectively. B is the batch size. N is the feature dimension. L_1 and L_2 are the number of time steps. Using the CMHA module as described in Equation <ref>, E_m and E_r are processed to produce E_spk ∈ ℝ^B × N × L_1. To align with the SepFormer <cit.> backbone network, employ a transformer encoder as the CMHA module in USEF-SepFormer. Subsequently, E_spk is combined with E_m through the fusion module. In USEF-SepFormer, FiLM is utilized for feature fusion:


    E_f = FiLM(E_m, E_spk)




    FiLM(E_m, E_spk) = γ(E_spk) ·E_m + β(E_spk)


where E_f ∈ ℝ^B × N × L_1 represents the fused features. The scaling and shifting vectors of FiLM are represented by γ(·) and β(·), respectively. E_f is then input into the separator.

USEF-SepFormer utilizes the SepFormer <cit.> backbone network as its separator. SepFormer <cit.> is is a masking-based, dual-path structure time-domain speech separation model. It divides long sequence features into equal-length chunks and performs intra-chunk and inter-chunk operations on these segmented features:


    E_intra = S_f + IntraTE(S^'_f)




    E_inter = E_intra + InterTE(E^'_intra)


where S_f ∈ ℝ^B × N × K × S denotes the segmentation result of E_f. S^'_f ∈ ℝ^(B × S) × K × N is a transformation of S_f. E_intra, E_inter ∈ ℝ^B × N × K × S are the outputs of the intra-chunk and inter-chunk modules, respectively. E^'_intra ∈ ℝ^(B × K) × S × N is a transformation of E_intra. IntraTE(·) and InterTE(·) are the intra and inter transformer encoder, respectively. After the dual-path network, USEF-SepFormer perform an overlap-add operation on E_inter to obtain E_o ∈ ℝ^B × N × L_1. E_o is the estimated mask for the target speaker. At last, the decoder processes the product of E_m and E_o and yielding the estimated target speaker speech:


    est = TConv1d(E_m * E_o)


where TConv1d(·) denotes the one-dimensional transposed convolution operation. est ∈ ℝ^B × 1 × T_1 denotes the estimation of the target speaker.





  §.§.§ USEF-TFGridNet

The diagram of USEF-TFGridNet is shown in Figure <ref>. USEF-TFGridNet uses two weight-sharing 2D convolutions to process the real and imaginary (RI) components of the STFT features for both the mixed speech and the reference speech:


    E_m = Conv2d(RI_m)




    E_r = Conv2d(RI_r)


where Conv2d(·) refers to 2D convolutions. RI_m ∈ ℝ^B × 2 × F × L_1 and RI_r ∈ ℝ^B × 2 × F × L_2 represent the stacked real and imaginary components of the STFT features for the mixed and reference speech, respectively, respectively. F is the embedding dimension for each T-F unit. L_1 and L_2 are the numbers of T-F units. The encoder results for the mixed and reference speech are E_m ∈ ℝ^B × C × F × L_1 and E_r ∈ ℝ^B × C × F × L_2. Through Equation <ref>, the CMHA module processes E_m and E_m, outputting E_spk ∈ ℝ^B × C × F × L_1. To align with the separator, USEF-TFGridNet uses the attention module from TF-GridNet <cit.> as its CMHA module. Next, we use concatenation as the feature fusion method to combine E_m and E_spk, resulting in the fused feature E_f ∈ ℝ^B × 2C × F × L_1. 

USEF-TFGridNet employs the TF-GridNet <cit.> backbone network as its separator. TF-GridNet <cit.> is a mapping-based T-F domain speech separation model. The backbone network of TF-GridNet <cit.> is composed of three key modules: the intra-frame full-band module, the sub-band temporal module, and the cross-frame self-attention module. The intra-frame full-band module processes data along the F dimension to capture full-band spectral and spatial information:


    U_f = Unfold(V(E^'_f))




    E_intra = E^'_f + TConv1d(BLSTM(U_f))


where E^'_f ∈ ℝ^B × 2C × F^'× L_1^' represents the zero-padded version of E_f. The reshaping operation, denoted as V(·), reshapes E^'_f into the shape ℝ^(B × L_1^') × F^'× 2C. Here, F^' = ⌈F-ks/hs⌉× hs + ks. The function Unfold(·) corresponds to the torch.unfold function, with U_f ∈ ℝ^(B × L_1^') × (F^'-ks/hs+1) × (2C × ks) as its output. The parameters ks, hs are the kernel size and stride of torch.unfold function, respectively. TConv1d(·) denotes a one-dimensional transposed convolution with kernel size ks and stride hs. BLSTM(·) means the bidirectional long short-term memory (BLSTM) <cit.> layer. The output of the intra-frame full-band module is E_intra ∈ ℝ^B × 2C × F^'× L_1^'. The sub-band temporal module then processes E_intra along the L_1^' dimension in the same manner as the intra-frame full-band module:


    E_sub = E_intra + V(Sub(E^'_intra))


where E^'_intra ∈ ℝ^(B × L_1^') × F^'× 2C represents a transformation of E_intra. The function Sub(·) denotes the operations within the sub-band temporal module, following the same procedures as in Equations (<ref>) - (<ref>). The output of the sub-band temporal module is  E_sub ∈ ℝ^B × 2C × F^'× L_1^'. After removing zero padding, E_sub is passed into the cross-frame self-attention module. This module is the same as the one in TF-GridNet <cit.>. It uses multi-head attention, allowing each T-F unit to attend directly to other relevant units:


    E_cross = Cross(E_sub[:,:,F,L_1])




    E_o = E_cross + E_sub[:,:,F,L_1]


where Cross(·) refers to the operations in the cross-frame self-attention module, which are the same as in TF-GridNet <cit.>. E_Cross, E_o ∈ ℝ^B × 2C × F × L_1 are the outputs of the cross-frame self-attention module and the separator, respectively. A 2D transposed convolution layer is then applied to adjust the number of channels in E_o back to 2. Finally, the result of the 2D transposed convolution is converted back to a waveform using iSTFT.


    est = iSTFT(TConv2d(E_o))


where TConv2d(·) denotes the 2D transposed convolution operation. est ∈ ℝ^B × 1 × T_1 denotes the estimation of the target speaker.





§ EXPERIMENTAL SETUP



 §.§ Datasets



  §.§.§ Anechoic Speech Mixtures

We validate our proposed methods on the WSJ0-2mix dataset <cit.>, a benchmark for speech separation and target speaker extraction in anechoic conditions. WSJ0-2mix is a two-speaker mixed dataset derived from the Wall Street Journal (WSJ0) corpus <cit.>. It consists of three subsets: the training set with 20,000 utterances from 101 speakers, the development set with 5,000 utterances from 101 speakers, and the test set with 3,000 utterances from 18 speakers. The training and development sets feature speakers from WSJ0 “si_tr_s”, while the test set includes speakers from WSJ0 “si_dt_05” and “si_et_05”. The speakers in the training and development sets are different from those in the test set. Utterances from two speakers are randomly selected to create mixed audio with a relative signal-to-noise ratio (SNR) between 0 dB and 5 dB. During training, the reference speech for the target speaker is randomly chosen and updated in each epoch. In the inference stage, we use the same reference speech as SpEx+ [1][1]<https://github.com/gemengtju/SpEx_Plus>.



  §.§.§ Noisy Speech Mixtures

We use the WSJ0 Hipster Ambient Mixtures (WHAM!) dataset <cit.> to evaluate our methods for noisy target speaker extraction. WHAM! is a noisy variant of the WSJ0-2mix dataset, featuring background noise recorded in urban environments such as coffee shops and parks. It preserves the same relative levels between the two speakers as WSJ0-2mix. Noise is added by sampling a random SNR value from a uniform distribution ranging from -6 to +3 dB. During training, the reference speech for the target speaker is randomly selected and updated each epoch. In the inference stage, we use the same reference speech as in the anechoic scenario.




  §.§.§ Noisy-Reverberant Speech Mixtures

We utilize the WHAMR! dataset <cit.> to test our algorithms for noisy reverberant target speaker extraction. WHAMR! extends the WSJ0-2mix dataset by reverberating each clean source and incorporating background noise. The reverberation time (T60) in these mixtures ranges from 0.2 to 1.0 seconds. The signal-to-noise ratio (SNR) between the louder speaker and the noise varies from -6 to 3 dB, the relative energy level between the two speakers ranges from -5 to 5 dB, and the speaker-to-array distance spans from 0.66 to 2.0 meters. The dataset includes 20,000 mixtures for training, 5,000 mixtures for development, and 3,000 mixtures for testing. During training, the direct-path signal from each speaker, recorded by the first microphone, serves as the target. This signal is also used as the reference for metric evaluation. The target speaker’s reference speech is randomly selected and updated each epoch during training. We use the same reference speech as in the anechoic scenario for inference.






 §.§ Network Configuration



  §.§.§ USEF-SepFormer

We configure USEF-SepFormer based on the parameter settings of SepFormer from SpeechBrain <cit.>. The encoder employs a 1D convolutional layer with a kernel size of 16 and a stride of 8. It has an input dimension of 1 and an output dimension of 256. The transformer encoder in the CMHA module consists of 4 layers, 8 parallel attention heads, and a 1024-dimensional feed-forward network. FiLM is implemented with two single-layer linear layers for scaling and shifting, with an input and output dimension of 256. The segmentation stage divides the input into chunks of size K = 250. For the SepFormer backbone network, the intra-transformer encoder and inter-transformer encoder each consist of 8 layers, 8 parallel attention heads, and a 1024-dimensional feed-forward network. The dual-path processing pipeline is repeated twice. The decoder uses a 1D transposed convolutional layer with the same kernel size and stride as the encoder. The hyperparameters for USEF-SepFormer are summarized in Table <ref>.








  §.§.§ USEF-TFGridNet


We configure USEF-TFGridNet based on the parameter settings of TF-GridNet from ESPNet <cit.>. For STFT, the window length is 16 ms, and the hop length is 8 ms. A 128-point Fourier transform is used to extract 65-dimensional complex STFT features at each frame. The 2D convolution layer has a kernel size of (3,3) and a stride of 1, with input and output dimensions of 2 and 128, respectively. The attention block in the CMHA module consists of 1 layer, 4 parallel attention heads, and a 512-dimensional feed-forward network. The kernel size and stride of the torch.unfold function are both 1. In the intra-frame full-band and sub-band temporal modules, the BLSTM layer has 256 units. The cross-frame self-attention module uses 1 layer, 4 parallel attention heads, and a 512-dimensional feed-forward network. The number of TF-GridNet blocks is 6. The 2D transposed convolution layer has the same kernel size and stride as the 2D convolution layer, with input and output dimensions of 256 and 2, respectively. The hyperparameters for USEF-TFGridNet are summarized in Table <ref>.



 §.§ Training Details


We trained our models using the Adam optimizer <cit.>, with an initial learning rate set to 1e-4. The learning rate was halved if the validation loss did not improve within 3 epochs. No dynamic mixing or data augmentation was applied during training. The speech clips were truncated to 4 seconds during training, while the full speech clips were evaluated during inference. In the evaluation phase, each speaker in the mixed speech is considered as the target speaker in turn. The models were trained to maximize the scale-invariant signal-to-distortion ratio (SI-SDR) <cit.>, which is defined as follows:


    s_T = <ŝ,s>s/||s||^2
    s_E = ŝ - s_T
    
    SI-SDR = 10||s_T||^2/||s_E||^2


where ŝ ∈ ℝ^1 × T represents the estimated target speaker speech, while s ∈ ℝ^1 × T represents the clean source speech. <s,s> denotes the power of the signal s.



 §.§ Evaluation Metrics

We use SI-SDR or SI-SDR improvement (SI-SDRi) <cit.> and signal-to-distortion ratio (SDR) or SDR improvement (SDRi) <cit.> as objective metrics to assess the accuracy of target speaker extraction. In addition to distortion metrics, we evaluate the estimated target speaker’s speech quality using the perceptual evaluation of subjective quality (PESQ) <cit.>. The PESQ values are calculated using the pypesq[2][2]<https://github.com/youngjamespark/python-pypesq> tookit, and the thop[3][3]<https://github.com/Lyken17/pytorch-OpCounter> toolkit is employed to measure the number of model parameters and computational cost.






§ RESULTS AND DISCUSSIONS


We present results from two sets of experiments. The first set is conducted on the WSJ0-2mix dataset, while the second set focuses on the WHAM! and WHAMR! datasets.



 §.§ Results on WSJ0-2mix



  §.§.§ Ablation Results of USEF-SepFormer With Different Hyper-Parameters

Table <ref> presents the ablation results of the USEF-SepFormer on WSJ0-2mix, using different model hyperparameters. Critical parameters of the USEF-SepFormer model are detailed in Table <ref>. Comparing row 1 with row 5 (or row 2 with row 6, row 3 with row 7, and row 4 with row 8), it is evident that the USEF-SepFormer model with 4 attention layers in the CMHA module outperforms the model with only 1 attention layer (C_l = 4 vs. C_l = 1). Further, comparing row 5 with row 6 (or rows 7 and 8) shows that the best performance is achieved when the SepFormer backbone module has 8 attention layers and a feed-forward network dimension of 1024 (S_l = 4, S_d = 1024). Finally, comparing rows 6 and 8 (or rows 2 and 4) reveals that with S_l = 4 and S_d = 1024, having 8 attention heads in the CMHA module of USEF-SepFormer yields better performance than having 4 attention heads (SI-SDRi = 19.9 dB vs. SI-SDRi = 19.2 dB). It may be because the CMHA module is consistent with the attention modules used in the separator, which ensures that the frame-level target speaker features and fusion feature reside in a highly similar feature space.



  §.§.§ Ablation Results of USEF-TFGridNet With Different Hyper-Parameters

Table <ref> presents the ablation results of the USEF-TFGridNet on WSJ0-2mix using different model hyper-parameters. The key parameters for the USEF-SepFormer model are detailed in Table <ref>. In USEF-TFGridNet, the self-attention parameters in the separator are consistent with those in TF-GridNet (S_l = 1, S_h = 4, S_d = 512). Comparing rows 1, 3, and 5, we observe that when the Unfold kernel size is set to 1  (K = 1), increasing the number of attention heads (C_h) or the dimension of the feed-forward network (FFN) (C_d) in the CMHA module does not enhance the model’s performance. Additionally, comparing rows 1 and 2, as well as rows 5 and 6, shows that when the FFN dimension is 512 (C_d = 512), using Unfold with a kernel size of 4 (K = 4) leads to decreased performance in the USEF-TFGridNet. Like USEF-Sepformer, USEF-TFGridNet achieves the best results (SI-SDRi = 23.3 dB) when the parameter settings of the CMHA module are consistent with those of the attention modules in the separator.




  §.§.§ Comparison With Previous Models

Tabel <ref> compares the performance of our proposed methods with previous speech separation (SS) and target speaker extraction (TSE) models on WSJ0-2mix. Our time-domain model, USEF-SepFormer, outperforms the best previous time-domain TSE models using speaker embedding, such as SpEx+, SpEx_pc, and X-SepFormer, with an SI-SDRi of 19.9 dB compared to 17.4 dB, 19.0 dB, and 19.1 dB, respectively. Additionally, compared to the best time-domain speaker-embedding-free TSE models like SEF-Net and VEVE, USEF-SepFormer achieves state-of-the-art results in both SDRi and SI-SDRi (20.2 dB and 19.9 dB). These results demonstrate that our enhancements to the previous SEF-Net model are practical, significantly improving the performance of time-domain TSE models.


Compared to previous SOTA T-F domain TSE models using speaker embedding, such as SMMA-Net and CIENet-mDPTNet, our proposed time-domain model USEF-TFGridNet demonstrates superior performance, with an SI-SDRi of 23.3 dB versus 20.4 dB and 21.4 dB, respectively. While the separators in SMMA-Net, CIENet-mDPTNet, and USEF-TFGridNet all process features along the channel dimension, SMMA-Net and CIENet-mDPTNet perform cross-attention on the real and imaginary parts of the reference speech’s STFT features and the mixed speech’s STFT features along the frequency dimension. The resulting features are then concatenated with the mixed speech’s STFT features along the channel dimension before increasing the channel dimension. In contrast, our USEF-TFGridNet model first increases the channel dimension and then applies cross-attention mechanisms directly along the channel dimension. This consistency in the modeling dimensions between the CMHA and separator modules leads to better performance. Overall, our proposed USEF-TFGridNet outperforms other time or T-F domain TSE models.





Notably, the backbone networks of X-SepFormer and X-TF-GridNet are identical to those used in our proposed USEF-SepFormer and USEF-TFGridNet. The critical difference lies in the auxiliary features used: X-SepFormer and X-TF-GridNet employ a pre-trained speaker verification model to extract X-vectors as auxiliary features for input into the separator, while USEF-SepFormer and USEF-TFGridNet directly input the acoustic features of the reference speech into the separator. This suggests that, in addition to speaker identity, other aspects of the reference speech, such as contextual information, can also be valuable for accurately extracting the target speaker.


We also compared our proposed models with several mainstream speech separation models on the WSJ0-2mix dataset. The current SOTA performance in the SS task is held by the MossFormer2 model, with an SI-SDRi of 24.1 dB. Previously, the best performance in the target speaker TSE task is achieved by the CIENet-mDPTNet model, with an SI-SDRi of 21.4 dB. Our proposed USEF-TFGridNet has set a new SOTA in the TSE task, achieving an SI-SDRi of 23.2 dB. This represents a 1.8 dB improvement over the CIENet-mDPTNet model, significantly narrowing the performance gap between the TSE and SS tasks from the previous 11.2% to the current 3.3% (from 2.7 dB to 0.8 dB).





  §.§.§ Comparison of Different Fusion Methods



Table <ref> presents the experimental results of USEF-SepFormer and USEF-TFGridNet with different feature fusion methods. Both FiLM and concatenation are effective for feature fusion in the USEF-TSE framework. Notably, FiLM generally outperforms concatenation in USEF-TSE, yielding better results (19.9 dB vs. 19.4 dB and 23.3 dB vs. 23.3 dB). However, in USEF-TFGridNet, while FiLM provides a performance boost, it also introduces additional network structures, slightly increasing the model’s parameters and computational complexity compared to concatenation.






  §.§.§ Comparison of Distributions of the number of test utterances at various dB ranges


Figure <ref> illustrates the distribution of SI-SDRi for 6,000 target speaker predictions (covering all speakers from 3,000 mixed speech samples). Predictions with an SI-SDRi value below 0 are considered poor cases, and fewer poor cases indicate better model performance. Notably, USEF-SepFormer and USEF-TFGridNet show significantly fewer poor cases compared to SEF-Net. Additionally, USEF-SepFormer and USEF-TFGridNet have a higher concentration of predictions in the 20 to 25 dB range than SEF-Net. The majority of USEF-TFGridNet’s predictions exceed 20 dB, with significantly more predictions above 25 dB compared to USEF-SepFormer and SEF-Net, indicating that USEF-TFGridNet achieves higher-quality target speaker estimation.



  §.§.§ Comparison of Different and Same Gender

 

Table <ref> reports the speaker extraction performance of our proposed methods on both different-gender and same-gender mixed speech. To facilitate comparison with other TSE models, we report the experimental results for only the first speaker. It is well-known that separating same-gender mixed speech is more challenging than different-gender speech, resulting in lower SDR and PESQ scores in same-gender scenarios. As shown in the table, both USEF-SepFormer and USEF-TFGridNet outperform SEF-Net in both scenarios. USEF-SepFormer demonstrates a relative improvement over SEF-Net of 22.9% in SDR and 11.2% in PESQ for different-gender scenarios, with an even more significant relative improvement of 30.3% in SDR and 11.5% in PESQ for same-gender scenarios. Additionally, USEF-TFGridNet notably reduces the SDR gap between same-gender and different-gender scenarios from 1.47 dB to 0.42 dB. This suggests that our proposed model is more effective and consistent in handling same-gender mixed speech scenarios.





 §.§ Results on WHAM and WHAMR!


Table <ref> compares the SDRi and SI-SDRi of our proposed methods with previous SS and TSE models on WHAM! and WHAMR!. USEF-SepFormer outperforms the previous best time-domain TSE model, SpEx++, on WHAM! (15.1 dB vs. 14.3 dB). However, it performs slightly worse on WHAMR! (11.3 dB vs. 11.7 dB). In contrast, T-F domain TSE models like USEF-TFGridNet and CIENet-mDPTNet significantly outperform time-domain models such as SpEx++ and USEF-SepFormer on both datasets. Moreover, the performance of time-domain TSE models degrades more in reverberant environments than T-F domain models. This suggests that T-F domain models may be better suited for target speaker extraction in complex scenarios than time-domain models. USEF-TFGridNet achieves SOTA performance on both the WHAM! and WHAMR! datasets. It shows a 5.3% relative improvement in SDRi and a 6.0% relative improvement in SI-SDRi over CIENet-mDPTNet on the WHAM! dataset. On the WHAMR! dataset, it demonstrates a 4.2% relative improvement in SDRi and a 2.5% relative improvement in SI-SDRi.


We also compared our proposed models with several leading speech SS models on the WHAM! and WHAMR! datasets. MossFormer2, which has achieved SOTA performance in SS tasks, reports an SI-SDRi of 18.1 dB and 17.0 dB on the WHAM! and WHAMR! datasets, respectively. Previously, the SOTA performance for TSE on these datasets is held by the CIENet-mDPTNet model, with SI-SDRi scores of 16.6 dB and 15.7 dB, respectively. Our proposed USEF-TFGridNet sets new state-of-the-art records in TSE tasks, achieving SI-SDRi scores of 17.6 dB and 16.1 dB on the WHAM! and WHAMR! datasets, respectively. Furthermore, USEF-TFGridNet reduces the performance gap between TSE and SS tasks from 8.3% and 7.6% to 2.8% and 5.3% on the WHAM! and WHAMR! datasets, respectively.
 


§ CONCLUSION


In this paper, we propose a Universal Speaker Embedding-Free Target Speaker Extraction (USEF-TSE) framework for monaural target speaker extraction. To validate the effectiveness of USEF-TSE, we integrate it with SepFormer and TF-GridNet to create two models: USEF-SepFormer and USEF-TFGridNet. Experimental results demonstrate that USEF-SepFormer and USEF-TFGridNet perform better than other time-domain and time-frequency domain target speaker extraction (TSE) models. Specifically, USEF-TFGridNet achieves state-of-the-art SI-SDRi scores of 23.3 dB on the WSJ0-2mix dataset, 17.6 dB on the WHAM! dataset, and 16.1 dB on the WHAMR! dataset for the TSE task. Furthermore, USEF-TFGridNet significantly narrows the performance gap between TSE and speech separation (SS) tasks, highlighting the robustness and effectiveness of our USEF-TSE framework under noisy and reverberant conditions. Future work will focus on addressing the limitations of the USEF-TSE framework and exploring potential issues, such as examining its performance on extremely large-scale speaker datasets, which represents a promising research direction.







 
 

















































 




















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