711 research outputs found
Linear and nonlinear adaptive filtering and their applications to speech intelligibility enhancement
Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates
This work addresses the problem of block-online processing for multi-channel
speech enhancement. Such processing is vital in scenarios with moving speakers
and/or when very short utterances are processed, e.g., in voice assistant
scenarios. We consider several variants of a system that performs beamforming
supported by DNN-based voice activity detection (VAD) followed by
post-filtering. The speaker is targeted through estimating relative transfer
functions between microphones. Each block of the input signals is processed
independently in order to make the method applicable in highly dynamic
environments. Owing to the short length of the processed block, the statistics
required by the beamformer are estimated less precisely. The influence of this
inaccuracy is studied and compared to the processing regime when recordings are
treated as one block (batch processing). The experimental evaluation of the
proposed method is performed on large datasets of CHiME-4 and on another
dataset featuring moving target speaker. The experiments are evaluated in terms
of objective and perceptual criteria (such as signal-to-interference ratio
(SIR) or perceptual evaluation of speech quality (PESQ), respectively).
Moreover, word error rate (WER) achieved by a baseline automatic speech
recognition system is evaluated, for which the enhancement method serves as a
front-end solution. The results indicate that the proposed method is robust
with respect to short length of the processed block. Significant improvements
in terms of the criteria and WER are observed even for the block length of 250
ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article
accepted for publication in IET Signal Processing journal. Original results
unchanged, additional experiments presented, refined discussion and
conclusion
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition
This article provides a unifying Bayesian network view on various approaches
for acoustic model adaptation, missing feature, and uncertainty decoding that
are well-known in the literature of robust automatic speech recognition. The
representatives of these classes can often be deduced from a Bayesian network
that extends the conventional hidden Markov models used in speech recognition.
These extensions, in turn, can in many cases be motivated from an underlying
observation model that relates clean and distorted feature vectors. By
converting the observation models into a Bayesian network representation, we
formulate the corresponding compensation rules leading to a unified view on
known derivations as well as to new formulations for certain approaches. The
generic Bayesian perspective provided in this contribution thus highlights
structural differences and similarities between the analyzed approaches
Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification
Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624
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