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Measurment of Impact of Environment Acoustics on Speech Recognition Accuracy
Paliesek, Jakub ; Žmolíková, Kateřina (referee) ; Szőke, Igor (advisor)
This bachelor thesis deals with investigation of impacts of acoustical parameters on automatic speech recognition (ASR) accuracy. Used ASRs were evaluated on Speecon, Temic and LibriSpeech corpuses. This work includes comparison of different versions of these data, which were created using retransmission in several rooms and artificial retransmission using impulse responses. These were created using methods Exponential sine sweep (ESS) and Maximum length sequence (MLS) for real rooms, as well as using Image source model (ISM) method, which generates artificial impulse responses. Output of the thesis is comparison of these types of retransmission. For ESS method, ASR accuracy for different lengths of the excitation signal is examined. Furthermore, the impact of relative position between source and receiver, presence of barriers and directionality of microphones is studied.
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Impact of Environment Acoustics on Speech Recognition Accuracy
Paliesek, Jakub ; Karafiát, Martin (referee) ; Szőke, Igor (advisor)
This diploma thesis deals with impact of room acoustics on automatic speech recognition (ASR) accuracy. Experiments were evaluated on speech corpus LibriSpeech and database of impulse responses and noise called ReverbDB. Used ASRs were based on Mini LibriSpeech recipe for Kaldi. First it was examined how well can ASR learn to transcribe in selected environments by using the same acoustic conditions during training and testing. Next, experiments were carried out with modifications of ASR architecture in order to achieve better robustness against new conditions by using methods for adapation to room acoustics - r-vectors and i-vectors. It was shown that recently proposed method of r-vectors is beneficial even when using real impulse responses for data augmentation.
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Impact of Environment Acoustics on Speech Recognition Accuracy
Paliesek, Jakub ; Karafiát, Martin (referee) ; Szőke, Igor (advisor)
This diploma thesis deals with impact of room acoustics on automatic speech recognition (ASR) accuracy. Experiments were evaluated on speech corpus LibriSpeech and database of impulse responses and noise called ReverbDB. Used ASRs were based on Mini LibriSpeech recipe for Kaldi. First it was examined how well can ASR learn to transcribe in selected environments by using the same acoustic conditions during training and testing. Next, experiments were carried out with modifications of ASR architecture in order to achieve better robustness against new conditions by using methods for adapation to room acoustics - r-vectors and i-vectors. It was shown that recently proposed method of r-vectors is beneficial even when using real impulse responses for data augmentation.
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Measurment of Impact of Environment Acoustics on Speech Recognition Accuracy
Paliesek, Jakub ; Žmolíková, Kateřina (referee) ; Szőke, Igor (advisor)
This bachelor thesis deals with investigation of impacts of acoustical parameters on automatic speech recognition (ASR) accuracy. Used ASRs were evaluated on Speecon, Temic and LibriSpeech corpuses. This work includes comparison of different versions of these data, which were created using retransmission in several rooms and artificial retransmission using impulse responses. These were created using methods Exponential sine sweep (ESS) and Maximum length sequence (MLS) for real rooms, as well as using Image source model (ISM) method, which generates artificial impulse responses. Output of the thesis is comparison of these types of retransmission. For ESS method, ASR accuracy for different lengths of the excitation signal is examined. Furthermore, the impact of relative position between source and receiver, presence of barriers and directionality of microphones is studied.
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