<prompt> Hlalosa hore na marang-rang a neural a ka sebelisoa joang molemong oa temoho ea puo.nan  <response>: Instruction in English: Explain how neural networks can be used for automated speech recognition. . Response in English: Neural networks are used in automated speech recognition systems to turn audio signals into text. They work by learning to recognize patterns in the audio data and then use that knowledge to transcribe new audio files. The process starts with the audio signal being divided into small units of time, usually around 20 milliseconds long. This audio data is then fed into the neural network which has been designed and trained to recognize specific sound patterns that make up human speech. These sound patterns are associated with phonemes, which are the smallest units of sound in language. The neural network is able to recognize each phoneme and their variations, by analyzing the audio datas spectral features such as pitch, volume, and other characteristics that differentiate one sound from another.Once the audio signal has been processed, and the phonemes have been recognized, the next step is to turn those phonemes into actual words. For this, another type of neural network called a Recurrent Neural Network RNN is used. RNNs analyze the sequence of phonemes and matches them with the most likely word or sequence of words. This is achieved by incorporating the context of the surrounding phonemes in the decision-making process. The system can also use a language model, which leverages knowledge of grammar and common word patterns, to further improve accuracy in the transcription process. The language model helps the neural network make better predictions by suggesting the most likely sequence of words given the previous words and the grammatical structure of the language.Overall, the use of neural networks in automated speech recognition allows for more accurate and efficient transcription of audio signals into text, making it an essential tool in voice-activated technologies, virtual assistants, and automated transcription services. . Response in Sesotho: Marang-rang a Neural a sebelisoa mekhoeng ea ho lemoha mantsoe ho fetola matshwao a medumo ho ba mongolo. Ba sebetsa ka ho ithuta ho lemoha lipaterone ho data ea molumo ebe ba sebelisa tsebo eo ho ngola lifaele tse ncha tsa audio. Tsebetso e qala ka letsoao la molumo le aroloa ka likarolo tse nyane tsa nako, hangata ho pota 20 milliseconds bolelele. Lintlha tsena tsa molumo li kenngoa ho netweke ea methapo ea kutlo e entsoeng le ho koetlisetsoa ho lemoha mekhoa e itseng ea molumo e etsang puo ea motho. Mekhoa ena ea molumo e amahanngoa le lifonomo, e leng likarolo tse nyenyane ka ho fetisisa tsa molumo puong. Neural network e khona ho lemoha fonome e ngoe le e ngoe le mefuta e fapaneng ea eona, ka ho hlahloba likarolo tsa pono ea molumo, molumo, le litšobotsi tse ling tse khethollang molumo o mong ho o mong. Hang ha letsoao la molumo le se le sebelitsoe, me le lifonomo li tsejoa, mohato o latelang ke ho fetola lifonomo tseo hore e be mantsoe a sebele. Bakeng sa sena, ho sebelisoa mofuta o mong oa marang-rang o bitsoang Recurrent Neural Network RNN. Li-RNN li sekaseka tatelano ea lifonomome me li li bapisa le lentsoe kapa tatellano ea mantsoe haholo. Sena se finyelloa ka ho kenyelletsa moelelo oa lifonomonomo tse potolohileng tšebetsong ea ho etsa liqeto. Tsamaiso e ka boela ea sebelisa mohlala oa puo, o matlafatsang tsebo ea sebōpeho-puo le mekhoa e tloaelehileng ea mantsoe, ho ntlafatsa ho feta mokhoa oa ho ngola mongolo. Moetso oa puo o thusa marang-rang a marang-rang ho etsa likhakanyo tse betere ka ho fana ka maikutlo a tatellano ea mantsoe e fanoeng mantsoe a fetileng le sebopeho sa puo ea puo. Ka kakaretso, tsebeliso ea marang-rang a neural temoho ea puo e ikemetseng e lumella ho ngoloa ka nepo le ka nepo ha matsoao a molumo hore e be sengoloa, e leng se etsang hore e be sesebelisoa sa bohlokoa ho mahlale a sebelisang lentsoe, bathusi ba sebele, le litsebeletso tsa khatiso tse ikemetseng. 