Microphone-array speech recognition via incremental map training
نویسندگان
چکیده
For a hidden Markov model (HMM) based speech recognition system it is desirable to combine enhancement of the acoustical signal and statistical representation of model parameters , ensuring both a high quality speech signal and an appropriately trained HMM. In this paper the incre-mental variant of maximum a posteriori (MAP) estimation is used to adjust the parameters of a talker-independent HMM-based speech recognition system to accurately recognize speech data acquired with a microphone-array. The approach is novel for a microphone-array speech recognition task in that a robust talker-independent model is derived from a baseline system using a relatively small amount of data for training. The results show that (1) MAP training signiicantly improves recognition performance compared to the baseline, and (2) beamforming signal enhancement out-performs single-channel enhancement before and after the adaptive MAP training.
منابع مشابه
Robust Hmm Training and Adaptation in Hands-free Speech Recognition
In this paper a challenging scenario is addressed in which a hands-free speech recognizer operates in a noisy office environment with either batch or incremental model adaptation. The application of a microphone array processing compensates only for part of the mismatch between training and testing acoustic conditions. In a previous work it was shown that the acoustic mismatch can be further re...
متن کاملContinuous Microphone Array Speech Recognition on Wall Street Journal Corpus
In this paper, we present a robust speech acquisition system to acquire continuous speech using a microphone array. A microphone array based speech recognition system is also presented to study the environmental interference due to reverberation, background noises and mismatch between the training and testing conditions. This is important in the context of smart meeting rooms of Augmented Multi...
متن کاملHands-free speech recognition using a filtered clean corpus and incremental HMM adaptation
A challenging scenario is addressed in which a hands-free speech recognizer operates in a noisy ooce environment with incremental model adaptation functionalities. The use of a single far microphone as well as that of a microphone array input are investigated. In a previous work it was shown that the acoustic mis-match, remaining after the application of microphone array processing, can be furt...
متن کاملUsing a real-time, tracking microphone array as input to an HMM speech recognizer
A major problem for speech recognition systems is relieving the talker of the need to use a close-talking, head-mounted or a deskstand microphone. A likely solution is the use of an array of microphones that can steer itself to the talker and can use a beamforming algorithm to overcome the reduced signal-to-noise ratio due to room acoustics. This paper reports results for a tracking, real-time ...
متن کاملSpeaker Adaptation in Continuous Speech Recognition Using MLLR-Based MAP Estimation
A variety of methods are used for speaker adaptation in speech recognition. In some techniques, such as MAP estimation, only the models with available training data are updated. Hence, large amounts of training data are required in order to have significant recognition improvements. In some others, such as MLLR, where several general transformations are applied to model clusters, the results ar...
متن کامل