Abstract
We present single-channel approaches to robust automatic speech recognition (ASR) in reverberant environments based on non-intrusive estimation of the clarity index (C 50). Our best performing method includes the estimated value of C 50 in the ASR feature vector and also uses C 50 to select the most suitable ASR acoustic model according to the reverberation level. We evaluate our method on the REVERB Challenge database employing two different C 50 estimators and show that our method outperforms the best baseline of the challenge achieved without unsupervised acoustic model adaptation, i.e. using multi-condition hidden Markov models (HMMs). Our approach achieves a 22.4 % relative word error rate reduction in comparison to the best baseline of the challenge.
Highlights
Automatic speech recognition (ASR) is increasingly being used as a tool for a wide range of applications in diverse acoustic conditions
A reverberant sound is created in enclosed spaces by reflections from surfaces which create a multipath sound propagation from the source to the receiver. This effect varies with the acoustic properties of the room and the source-receiver distance, and it is characterized by the room impulse response (RIR)
The ASR evaluation tool is based on the hidden Markov model tool kit (HTK) provided by the REVERB Challenge
Summary
Automatic speech recognition (ASR) is increasingly being used as a tool for a wide range of applications in diverse acoustic conditions (e.g. health care transcriptions, automatic translation, voicemail-to-text, and voice interface for command and control). Distant speech recognition is essential for natural and comfortable human-machine voice interfaces such as used in, for example, the automotive sector and smartphone mobile applications. A reverberant sound is created in enclosed spaces by reflections from surfaces which create a multipath sound propagation from the source to the receiver. This effect varies with the acoustic properties of the room and the source-receiver distance, and it is characterized by the room impulse response (RIR).
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