7,259 research outputs found
Hybrid acoustic models for distant and multichannel large vocabulary speech recognition
We investigate the application of deep neural network (DNN)hidden Markov model (HMM) hybrid acoustic models for far-field speech recognition of meetings recorded using microphone arrays. We show that the hybrid models achieve significantly better accuracy than conventional systems based on Gaussian mixture models (GMMs). We observe up to 8% absolute word error rate (WER) reduction from a discriminatively trained GMM baseline when using a single distant microphone, and between 4–6 % absolute WER reduction when using beamforming on various combinations of array channels. By training the networks on audio from multiple channels, we find the networks can recover significant part of accuracy difference between the single distant microphone and beamformed configurations. Finally, we show that the accuracy of a network recognising speech from a single distant microphone can approach that of a multi-microphone setup by training with data from other microphones
Learning to Rank Microphones for Distant Speech Recognition
Fully exploiting ad-hoc microphone networks for distant speech recognition is
still an open issue. Empirical evidence shows that being able to select the
best microphone leads to significant improvements in recognition without any
additional effort on front-end processing. Current channel selection techniques
either rely on signal, decoder or posterior-based features. Signal-based
features are inexpensive to compute but do not always correlate with
recognition performance. Instead decoder and posterior-based features exhibit
better correlation but require substantial computational resources. In this
work, we tackle the channel selection problem by proposing MicRank, a learning
to rank framework where a neural network is trained to rank the available
channels using directly the recognition performance on the training set. The
proposed approach is agnostic with respect to the array geometry and type of
recognition back-end. We investigate different learning to rank strategies
using a synthetic dataset developed on purpose and the CHiME-6 data. Results
show that the proposed approach is able to considerably improve over previous
selection techniques, reaching comparable and in some instances better
performance than oracle signal-based measures
Neural Network based Regression for Robust Overlapping Speech Recognition using Microphone Arrays
This paper investigates a neural network based acoustic feature mapping to extract robust features for automatic speech recognition (ASR) of overlapping speech. In our preliminary studies, we trained neural networks to learn the mapping from log mel filter bank energies (MFBEs) extracted from the distant microphone recordings, including multiple overlapping speakers, to log MFBEs extracted from the clean speech signal. In this paper, we explore the mapping of higher order mel-filterbank cepstral coefficients (MFCC) to lower order coefficients. We also investigate the mapping of features from both target and interfering distant sound sources to the clean target features. This is achieved by using the microphone array to extract features from both the direction of the target and interfering sound sources. We demonstrate the effectiveness of the proposed approach through extensive evaluations on the MONC corpus, which includes both non-overlapping single speaker and overlapping multi-speaker conditions
Realistic multi-microphone data simulation for distant speech recognition
The availability of realistic simulated corpora is of key importance for the
future progress of distant speech recognition technology. The reliability,
flexibility and low computational cost of a data simulation process may
ultimately allow researchers to train, tune and test different techniques in a
variety of acoustic scenarios, avoiding the laborious effort of directly
recording real data from the targeted environment.
In the last decade, several simulated corpora have been released to the
research community, including the data-sets distributed in the context of
projects and international challenges, such as CHiME and REVERB. These efforts
were extremely useful to derive baselines and common evaluation frameworks for
comparison purposes. At the same time, in many cases they highlighted the need
of a better coherence between real and simulated conditions.
In this paper, we examine this issue and we describe our approach to the
generation of realistic corpora in a domestic context. Experimental validation,
conducted in a multi-microphone scenario, shows that a comparable performance
trend can be observed with both real and simulated data across different
recognition frameworks, acoustic models, as well as multi-microphone processing
techniques.Comment: Proc. of Interspeech 201
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning
In this work, we investigated the teacher-student training paradigm to train
a fully learnable multi-channel acoustic model for far-field automatic speech
recognition (ASR). Using a large offline teacher model trained on beamformed
audio, we trained a simpler multi-channel student acoustic model used in the
speech recognition system. For the student, both multi-channel feature
extraction layers and the higher classification layers were jointly trained
using the logits from the teacher model. In our experiments, compared to a
baseline model trained on about 600 hours of transcribed data, a relative
word-error rate (WER) reduction of about 27.3% was achieved when using an
additional 1800 hours of untranscribed data. We also investigated the benefit
of pre-training the multi-channel front end to output the beamformed log-mel
filter bank energies (LFBE) using L2 loss. We find that pre-training improves
the word error rate by 10.7% when compared to a multi-channel model directly
initialized with a beamformer and mel-filter bank coefficients for the front
end. Finally, combining pre-training and teacher-student training produces a
WER reduction of 31% compared to our baseline.Comment: To appear in ICASSP 202
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