10,637 research outputs found
An evaluation of intrusive instrumental intelligibility metrics
Instrumental intelligibility metrics are commonly used as an alternative to
listening tests. This paper evaluates 12 monaural intrusive intelligibility
metrics: SII, HEGP, CSII, HASPI, NCM, QSTI, STOI, ESTOI, MIKNN, SIMI, SIIB, and
. In addition, this paper investigates the ability of
intelligibility metrics to generalize to new types of distortions and analyzes
why the top performing metrics have high performance. The intelligibility data
were obtained from 11 listening tests described in the literature. The stimuli
included Dutch, Danish, and English speech that was distorted by additive
noise, reverberation, competing talkers, pre-processing enhancement, and
post-processing enhancement. SIIB and HASPI had the highest performance
achieving a correlation with listening test scores on average of
and , respectively. The high performance of SIIB may, in part, be
the result of SIIBs developers having access to all the intelligibility data
considered in the evaluation. The results show that intelligibility metrics
tend to perform poorly on data sets that were not used during their
development. By modifying the original implementations of SIIB and STOI, the
advantage of reducing statistical dependencies between input features is
demonstrated. Additionally, the paper presents a new version of SIIB called
, which has similar performance to SIIB and HASPI,
but takes less time to compute by two orders of magnitude.Comment: Published in IEEE/ACM Transactions on Audio, Speech, and Language
Processing, 201
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
Acoustic Echo and Noise Cancellation System for Hand-Free Telecommunication using Variable Step Size Algorithms
In this paper, acoustic echo cancellation with doubletalk detection system is implemented for a hand-free telecommunication system using Matlab. Here adaptive noise canceller with blind source separation (ANC-BSS) system is proposed to remove both background noise and far-end speaker echo signal in presence of double-talk. During the absence of double-talk, far-end speaker echo signal is cancelled by adaptive echo canceller. Both adaptive noise canceller and adaptive echo canceller are implemented using LMS, NLMS, VSLMS and VSNLMS algorithms. The normalized cross-correlation method is used for double-talk detection. VSNLMS has shown its superiority over all other algorithms both for double-talk and in absence of double-talk. During the absence of double-talk it shows its superiority in terms of increment in ERLE and decrement in misalignment. In presence of double-talk, it shows improvement in SNR of near-end speaker signal
DNN adaptation by automatic quality estimation of ASR hypotheses
In this paper we propose to exploit the automatic Quality Estimation (QE) of
ASR hypotheses to perform the unsupervised adaptation of a deep neural network
modeling acoustic probabilities. Our hypothesis is that significant
improvements can be achieved by: i)automatically transcribing the evaluation
data we are currently trying to recognise, and ii) selecting from it a subset
of "good quality" instances based on the word error rate (WER) scores predicted
by a QE component. To validate this hypothesis, we run several experiments on
the evaluation data sets released for the CHiME-3 challenge. First, we operate
in oracle conditions in which manual transcriptions of the evaluation data are
available, thus allowing us to compute the "true" sentence WER. In this
scenario, we perform the adaptation with variable amounts of data, which are
characterised by different levels of quality. Then, we move to realistic
conditions in which the manual transcriptions of the evaluation data are not
available. In this case, the adaptation is performed on data selected according
to the WER scores "predicted" by a QE component. Our results indicate that: i)
QE predictions allow us to closely approximate the adaptation results obtained
in oracle conditions, and ii) the overall ASR performance based on the proposed
QE-driven adaptation method is significantly better than the strong, most
recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201
Effects of noise suppression and envelope dynamic range compression on the intelligibility of vocoded sentences for a tonal language
Vocoder simulation studies have suggested that the carrier signal type employed affects the intelligibility of vocoded speech. The present work further assessed how carrier signal type interacts with additional signal processing, namely, single-channel noise suppression and envelope dynamic range compression, in determining the intelligibility of vocoder simulations. In Experiment 1, Mandarin sentences that had been corrupted by speech spectrum-shaped noise (SSN) or two-talker babble (2TB) were processed by one of four single-channel noise-suppression algorithms before undergoing tone-vocoded (TV) or noise-vocoded (NV) processing. In Experiment 2, dynamic ranges of multiband envelope waveforms were compressed by scaling of the mean-removed envelope waveforms with a compression factor before undergoing TV or NV processing. TV Mandarin sentences yielded higher intelligibility scores with normal-hearing (NH) listeners than did noise-vocoded sentences. The intelligibility advantage of noise-suppressed vocoded speech depended on the masker type (SSN vs 2TB). NV speech was more negatively influenced by envelope dynamic range compression than was TV speech. These findings suggest that an interactional effect exists between the carrier signal type employed in the vocoding process and envelope distortion caused by signal processing
Towards An Intelligent Fuzzy Based Multimodal Two Stage Speech Enhancement System
This thesis presents a novel two stage multimodal speech enhancement system, making use of both visual and audio information to filter speech, and explores the extension of
this system with the use of fuzzy logic to demonstrate proof of concept for an envisaged autonomous, adaptive, and context aware multimodal system. The design of the proposed cognitively inspired framework is scalable, meaning that it is possible for the techniques used in individual parts of the system to be upgraded and there is scope for the initial framework presented here to be expanded.
In the proposed system, the concept of single modality two stage filtering is extended to include the visual modality. Noisy speech information received by a microphone array is first pre-processed by visually derived Wiener filtering employing the novel use of the Gaussian Mixture Regression (GMR) technique, making use of associated visual speech information, extracted using a state of the art Semi Adaptive Appearance Models (SAAM) based lip tracking approach. This pre-processed speech is then enhanced further by audio only beamforming using a state of the art Transfer Function Generalised Sidelobe Canceller (TFGSC) approach. This results in a system which is designed to function in challenging noisy speech environments (using speech sentences with different speakers from the GRID corpus and a range of noise recordings), and both objective and subjective test results (employing the widely used Perceptual Evaluation of Speech Quality (PESQ) measure, a composite objective measure, and subjective listening tests), showing that this initial system is capable of delivering very encouraging results with regard to filtering speech mixtures in difficult reverberant speech environments.
Some limitations of this initial framework are identified, and the extension of this multimodal system is explored, with the development of a fuzzy logic based framework and a proof of concept demonstration implemented. Results show that this proposed autonomous,adaptive, and context aware multimodal framework is capable of delivering very positive results in difficult noisy speech environments, with cognitively inspired use of audio and visual information, depending on environmental conditions. Finally some concluding remarks
are made along with proposals for future work
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