Digital processing of speech signals

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Nonparametrics Statistical Methods Based On Ranks

2017 25th European Signal Processing Conference (EUSIPCO)

In recent years, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques and automatic speech recognition (ASR) techniques that are robust to reverberation. In this paper, we describe the REVERB challenge, which is an evaluation campaign that was designed to evaluate such speech enhancement (SE) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions. Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single-channel scenario, a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation, based on both real, single-channel, and multichannel recordings. This challenge attracted 27 papers, which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes. This paper describes the problems dealt within the challenge, provides an overview of the submitted systems, and scrutinizes them to clarify what current processing strategies appear effective in reverberant speech processing.

/pdf/a-summary-of-the-reverb-challenge-state-of-the-art-and-4d7rvajsep.pdf

A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

We present PEFAC, a fundamental frequency estimation algorithm for speech that is able to identify voiced frames and estimate pitch reliably even at negative signal-to-noise ratios. The algorithm combines a normalization stage, to remove channel dependency and to attenuate strong noise components, with a harmonic summing filter applied in the log-frequency power spectral domain, the impulse response of which is chosen to sum the energy of the fundamental frequency harmonics while attenuating smoothly-varying noise components. Temporal continuity constraints are applied to the selected pitch candidates and a voiced speech probability is computed from the likelihood ratio of two classifiers, one for voiced speech and one for unvoiced speech/silence. We compare the performance of our algorithm with that of other widely used algorithms and demonstrate that it performs well in both high and low levels of additive noise.

PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise

A data-driven non-intrusive measure of speech quality and intelligibility

Several intrusive measures of reverberation can be computed from measured and simulated room impulse responses, over the full frequency band or for each individual mel-frequency subband. It is initially shown that full-band clarity index ${\rm{C}}_{50}$ is the most correlated measure on average with reverberant speech recognition performance. This corroborates previous findings but now for the dataset to be used in this study. We extend the previous findings to show that ${\rm{C}}_{50}$ also exhibits the highest mutual information on average. Motivated by these extended findings, a nonintrusive room acoustic (NIRA) estimation method is proposed to estimate ${\rm{C}}_{50}$ from only the reverberant speech signal. The NIRA method is a data-driven approach based on computing a number of features from the speech signal and it employs these features to train a model used to perform the estimation. The choice of features and learning techniques are explored in this work using an evaluation set which comprises approximately 100 000 different reverberant signals (around 93 h of speech) including reverberation from measured and simulated room impulse responses. The feature importance of each feature with respect to the estimation of the target ${\rm{C}}_{50}$ is analysed following two different approaches. In both cases, the newly chosen set of features shows high importance for the target. The best ${\rm{C}}_{50}$ estimator provides a root-mean-square deviation around 3 dB on average for all reverberant test environments.

/pdf/a-single-channel-non-intrusive-c50-estimator-correlated-with-3m4pap5rio.pdf

A single-channel non-intrusive C50 estimator correlated with speech recognition performance

We show corroborating evidence that, among a set of common acoustic parameters, the clarity index C 50 provides a measure of reverberation that is well correlated with speech recognition accuracy. We also present a data driven method for non-intrusive C 50 parameter estimation from a single channel speech signal. The method extracts a number of features from the speech signal and uses a binary regression tree, trained on appropriate training data, to estimate the C 50 . Evaluation is carried out using speech utterances convolved with real and simulated room impulse responses, and additive babble noise. The new method outperforms a baseline approach in our evaluation.

Non-intrusive estimation of the level of reverberation in speech

We propose a data driven, non-intrusive method for speech intelligibility estimation. We begin with a large set of speech signal specific features and use a dimensionality reduction approach based on correlation and principal component analysis to find the most relevant features for intelligibility prediction. These are then used to train a Gaussian mixture model from which the intelligibility of unseen data is inferred. Experimental results show that our method gives a correlation with subjective intelligibility of 0.92 and a correlation of 0.96 with the ANSI standard Speech Intelligibility Index.

/pdf/data-driven-method-for-non-intrusive-speech-intelligibility-1fx1p7b4ee.pdf

Data driven method for non-intrusive speech intelligibility estimation

Pitch estimation has a central role in many speech processing applications. In voiced speech, pitch can be objectively defined as the rate of vibration of the vocal folds. However, pitch is an inherently subjective quantity and cannot be directly measured from the speech signal. It is a nonlinear function of the signal's spectral and temporal energy distribution. A number of methods for pitch estimation have been developed but none can claim to work accurately in the presence of high levels of additive noise or reverberation. Any system of practical importance must be robust to additive noise and reverberation as these are encountered frequently in the field of operation of voice telecommunications systems. In non-intrusive speech quality measurement algorithms, such as the P.563 and LCQA, pitch is used as a feature for quality assessment. The accuracy of this feature in noisy speech signals will be shown to correlate with the accuracy of the objective measure of the quality of the speech signal. In this paper we evaluate the performance of four established state-of-the-art algorithms for pitch estimation in additive noise and reverberation. Furthermore, we show how accurate estimation of the pitch of a speech signal can influence objective speech quality measurement algorithms.

/pdf/evaluation-of-pitch-estimation-in-noisy-speech-for-27ed6wfcqt.pdf

Dushyant Sharma

Papers

A data-driven non-intrusive measure of speech quality and intelligibility

A single-channel non-intrusive C50 estimator correlated with speech recognition performance

Non-intrusive estimation of the level of reverberation in speech

Data driven method for non-intrusive speech intelligibility estimation

Evaluation of pitch estimation in noisy speech for application in non-intrusive speech quality assessment