137 research outputs found
Glottal-synchronous speech processing
Glottal-synchronous speech processing is a field of speech science where the pseudoperiodicity
of voiced speech is exploited. Traditionally, speech processing involves segmenting
and processing short speech frames of predefined length; this may fail to exploit the inherent
periodic structure of voiced speech which glottal-synchronous speech frames have
the potential to harness. Glottal-synchronous frames are often derived from the glottal
closure instants (GCIs) and glottal opening instants (GOIs).
The SIGMA algorithm was developed for the detection of GCIs and GOIs from
the Electroglottograph signal with a measured accuracy of up to 99.59%. For GCI and
GOI detection from speech signals, the YAGA algorithm provides a measured accuracy
of up to 99.84%. Multichannel speech-based approaches are shown to be more robust to
reverberation than single-channel algorithms.
The GCIs are applied to real-world applications including speech dereverberation,
where SNR is improved by up to 5 dB, and to prosodic manipulation where the importance
of voicing detection in glottal-synchronous algorithms is demonstrated by subjective
testing. The GCIs are further exploited in a new area of data-driven speech modelling,
providing new insights into speech production and a set of tools to aid deployment into
real-world applications. The technique is shown to be applicable in areas of speech coding,
identification and artificial bandwidth extension of telephone speec
Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation
Several speech processing systems have demonstrated considerable performance
improvements when deep complex neural networks (DCNN) are coupled with
self-attention (SA) networks. However, the majority of DCNN-based studies on
speech dereverberation that employ self-attention do not explicitly account for
the inter-dependencies between real and imaginary features when computing
attention. In this study, we propose a complex-valued T-F attention (TFA)
module that models spectral and temporal dependencies by computing
two-dimensional attention maps across time and frequency dimensions. We
validate the effectiveness of our proposed complex-valued TFA module with the
deep complex convolutional recurrent network (DCCRN) using the REVERB challenge
corpus. Experimental findings indicate that integrating our complex-TFA module
with DCCRN improves overall speech quality and performance of back-end speech
applications, such as automatic speech recognition, compared to earlier
approaches for self-attention.Comment: Interspeech 2022: ISCA Best Student Paper Award Finalis
Spatial features of reverberant speech: estimation and application to recognition and diarization
Distant talking scenarios, such as hands-free calling or teleconference meetings, are essential for natural and comfortable human-machine interaction and they are being increasingly used in multiple contexts. The acquired speech signal in such scenarios is reverberant and affected by additive noise. This signal distortion degrades the performance of speech recognition and diarization systems creating troublesome human-machine interactions.This thesis proposes a method to non-intrusively estimate room acoustic parameters, paying special attention to a room acoustic parameter highly correlated with speech recognition degradation: clarity index. In addition, a method to provide information regarding the estimation accuracy is proposed. An analysis of the phoneme recognition performance for multiple reverberant environments is presented, from which a confusability metric for each phoneme is derived. This confusability metric is then employed to improve reverberant speech recognition performance. Additionally, room acoustic parameters can as well be used in speech recognition to provide robustness against reverberation. A method to exploit clarity index estimates in order to perform reverberant speech recognition is introduced.
Finally, room acoustic parameters can also be used to diarize reverberant speech. A room acoustic parameter is proposed to be used as an additional source of information for single-channel diarization purposes in reverberant environments. In multi-channel environments, the time delay of arrival is a feature commonly used to diarize the input speech, however the computation of this feature is affected by reverberation. A method is presented to model the time delay of arrival in a robust manner so that speaker diarization is more accurately performed.Open Acces
A Study into Speech Enhancement Techniques in Adverse Environment
This dissertation developed speech enhancement techniques that improve the speech quality in applications such as mobile communications, teleconferencing and smart loudspeakers. For these applications it is necessary to suppress noise and reverberation. Thus the contribution in this dissertation is twofold: single channel speech enhancement system which exploits the temporal and spectral diversity of the received microphone signal for noise suppression and multi-channel speech enhancement method with the ability to employ spatial diversity to reduce reverberation
Combining pulse-based features for rejecting far-field speech in a HMM-based Voice Activity Detector. Computers & Electrical Engineering (CAEE).
Nowadays, several computational techniques for speech recognition have been proposed. These techniques suppose an important improvement in real time applications where speaker interacts with speech recognition systems. Although researchers proposed many methods, none of them solve the high false alarm problem when far-field speakers interfere in a human-machine conversation. This paper presents a two-class (speech and non-speech classes) decision-tree based approach for combining new speech pulse features in a VAD (Voice Activity Detector) for rejecting far-field speech in speech recognition systems. This Decision Tree is applied over the speech pulses obtained by a baseline VAD composed of a frame feature extractor, a HMM-based (Hidden Markov Model) segmentation module and a pulse detector. The paper also presents a detailed analysis of a great amount of features for discriminating between close and far-field speech. The detection error obtained with the proposed VAD is the lowest compared to other well-known VAD
A Subband-Based SVM Front-End for Robust ASR
This work proposes a novel support vector machine (SVM) based robust
automatic speech recognition (ASR) front-end that operates on an ensemble of
the subband components of high-dimensional acoustic waveforms. The key issues
of selecting the appropriate SVM kernels for classification in frequency
subbands and the combination of individual subband classifiers using ensemble
methods are addressed. The proposed front-end is compared with state-of-the-art
ASR front-ends in terms of robustness to additive noise and linear filtering.
Experiments performed on the TIMIT phoneme classification task demonstrate the
benefits of the proposed subband based SVM front-end: it outperforms the
standard cepstral front-end in the presence of noise and linear filtering for
signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed
front-end with a conventional front-end such as MFCC yields further
improvements over the individual front ends across the full range of noise
levels
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