Far-field Automatic Speech Recognition Research Articles

Curriculum Learning based approaches for robust end-to-end far-field speech recognition

Performance of Automatic Speech Recognition (ASR) systems is known to suffer considerable degradation when exposed to Far-Field speech data capture. Consequently, far-field ASR has received considerable attention in recent years. Motivated by our recent work using Curriculum Learning (CL) based strategies to improve Speaker Identification (SID) under noisy and degraded conditions, this study proposes a novel approach to improve far-field ASR using CL based approaches. Specifically, we propose using a CL based approach for training a Bidirectional Long Short Term Memory (BLSTM) based ASR network trained using the Connectionist Temporal Classification (CTC) objective function. We initiate the training with comparatively easier near-field data, and include more diverse (difficult) far-field data progressively in the later stages of training. These proposed approaches are shown to significantly outperform the baseline BLSTM ASR system, and offer relative reductions in WERs of up to +7.3% and +10.1% for the dev and eval sets of the AMI far-field voice capture corpus.

Far-Field Automatic Speech Recognition

The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase in attention in science and industry, which caused or was caused by an equally significant improvement in recognition accuracy. Meanwhile, it has entered the consumer market with digital home assistants with a spoken language interface being its most prominent application. Speech recorded at a distance is affected by various acoustic distortions, and consequently, quite different processing pipelines have emerged compared with ASR for close-talk speech. A signal enhancement front end for dereverberation, source separation, and acoustic beamforming is employed to clean up the speech, and the back-end ASR engine is robustified by multicondition training and adaptation. We will also describe the so-called end-to-end approach to ASR, which is a new promising architecture that has recently been extended to the far-field scenario. This tutorial article gives an account of the algorithms used to enable accurate speech recognition from a distance, and it will be seen that, although deep learning has a significant share in the technological breakthroughs, a clever combination with traditional signal processing can lead to surprisingly effective solutions.

Multi-Stream End-to-End Speech Recognition

Attention-based methods and Connectionist Temporal Classification (CTC) network have been promising research directions for end-to-end (E2E) Automatic Speech Recognition (ASR). The joint CTC/Attention model has achieved great success by utilizing both architectures during multi-task training and joint decoding. In this article, we present a multi-stream framework based on joint CTC/Attention E2E ASR with parallel streams represented by separate encoders aiming to capture diverse information. On top of the regular attention networks, the Hierarchical Attention Network (HAN) is introduced to steer the decoder toward the most informative encoders. A separate CTC network is assigned to each stream to force monotonic alignments. Two representative framework have been proposed and discussed, which are Multi-Encoder Multi-Resolution (MEM-Res) framework and Multi-Encoder Multi-Array (MEM-Array) framework, respectively. In MEM-Res framework, two heterogeneous encoders with different architectures, temporal resolutions and separate CTC networks work in parallel to extract complementary information from same acoustics. Experiments are conducted on Wall Street Journal (WSJ) and CHiME-4, resulting in relative Word Error Rate (WER) reduction of $\text{18.0}\!-\!\text{32.1}\%$ and the best WER of $\text{3.6}\%$ in the WSJ eval92 test set. The MEM-Array framework aims at improving the far-field ASR robustness using multiple microphone arrays which are activated by separate encoders. Compared with the best single-array results, the proposed framework has achieved relative WER reduction of $\text{3.7}\%$ and $\text{9.7}\%$ in AMI and DIRHA multi-array corpora, respectively, which also outperforms conventional fusion strategies.

Beamforming With a Maximum Negentropy Criterion

In this paper, we address a beamforming application based on the capture of far-field speech data from a single speaker in a real meeting room. After the position of the speaker is estimated by a speaker tracking system, we construct a subband-domain beamformer in generalized sidelobe canceller (GSC) configuration. In contrast to conventional practice, we then optimize the active weight vectors of the GSC so as to obtain an output signal with maximum negentropy (MN). This implies the beamformer output should be as non-Gaussian as possible. For calculating negentropy, we consider the Gamma and the generalized Gaussian (GG) pdfs. After MN beamforming, Zelinski postfiltering is performed to further enhance the speech by removing residual noise. Our beamforming algorithm can suppress noise and reverberation without the signal cancellation problems encountered in the conventional beamforming algorithms. We demonstrate this fact through a set of acoustic simulations. Moreover, we show the effectiveness of our proposed technique through a series of far-field automatic speech recognition experiments on the Multi-Channel Wall Street Journal Audio Visual Corpus (MC-WSJ-AV), a corpus of data captured with real far-field sensors, in a realistic acoustic environment, and spoken by real speakers. On the MC-WSJ-AV evaluation data, the delay-and-sum beamformer with postfiltering achieved a word error rate (WER) of 16.5%. MN beamforming with the Gamma pdf achieved a 15.8% WER, which was further reduced to 13.2% with the GG pdf, whereas the simple delay-and-sum beamformer provided a WER of 17.8%. To the best of our knowledge, no lower error rates at present have been reported in the literature on this automatic speech recognition (ASR) task.

Adaptive Beamforming With a Minimum Mutual Information Criterion

In this paper, we consider an acoustic beamforming application where two speakers are simultaneously active. We construct one subband-domain beamformer in generalized sidelobe canceller (GSC) configuration for each source. In contrast to normal practice, we then jointly optimize the active weight vectors of both GSCs to obtain two output signals with minimum mutual information (MMI). Assuming that the subband snapshots are Gaussian-distributed, this MMI criterion reduces to the requirement that the cross-correlation coefficient of the subband outputs of the two GSCs vanishes. We also compare separation performance under the Gaussian assumption with that obtained from several super-Gaussian probability density functions (pdfs), namely, the Laplace and pdfs. Our proposed technique provides effective nulling of the undesired source, but without the signal cancellation problems seen in conventional beamforming. Moreover, our technique does not suffer from the source permutation and scaling ambiguities encountered in conventional blind source separation algorithms. We demonstrate the effectiveness of our proposed technique through a series of far-field automatic speech recognition experiments on data from the PASCAL Speech Separation Challenge (SSC). On the SSC development data, the simple delay-and-sum beamformer achieves a word error rate (WER) of 70.4%. The MMI beamformer under a Gaussian assumption achieves a 55.2% WER, which is further reduced to 52.0% with a pdf, whereas the WER for data recorded with a close-talking microphone is 21.6%.