We present our approach to unsupervised training of speech recognizers. Our approach iteratively adjusts sound units that are optimized for the acoustic domain of interest. We thus enable the use of speech recognizers for applications in speech domains where transcriptions do not exist. The resulting recognizer is a state-of-the-art recognizer on the optimized units. Specifically we propose building HMM-based speech recognizers without transcribed data by formulating the HMM training as an optimization over both the parameter and transcription sequence space. Audio is then transcribed into these self-organizing units (SOUs). We describe how SOU training can be easily implemented using existing HMM recognition tools. We tested the effectiveness of SOUs on the task of topic classification on the Switchboard and Fisher corpora. On the Switchboard corpus, the unsupervised HMM-based SOU recognizer, initialized with a segmental tokenizer, performed competitively with an HMM-based phoneme recognizer trained with 1h of transcribed data, and outperformed the Brno University of Technology (BUT) Hungarian phoneme recognizer (Schwartz et al., 2004). We also report improvements, including the use of context dependent acoustic models and lattice-based features, that together reduce the topic verification equal error rate from 12% to 7%. In addition to discussing the effectiveness of the SOU approach, we describe how we analyzed some selected SOU n-grams and found that they were highly correlated with keywords, demonstrating the ability of the SOU technology to discover topic relevant keywords. 
Unsupervised training of an HMM-based self-organizing unit recognizer with applications to topic classification and keyword discovery