This letter proposes a perceptual metric for speech quality evaluation, which is suitable, as a loss function, for training deep learning methods. This metric, derived from the perceptual evaluation of the speech quality algorithm, is computed in a per-frame basis and from the power spectra of the reference and processed speech signal. Thus, two disturbance terms, which account for distortion once auditory masking and threshold effects are factored in, amend the mean square error (MSE) loss function by introducing perceptual criteria based on human psychoacoustics. The proposed loss function is evaluated for noisy speech enhancement with deep neural networks. Experimental results show that our metric achieves significant gains in speech quality (evaluated using an objective metric and a listening test) when compared to using MSE or other perceptual-based loss functions from the literature.

This review summarises the status of silent speech interface (SSI) research. SSIs rely on non-acoustic biosignals generated by the human body during speech production to enable communication whenever normal verbal communication is not possible or not desirable. In this review, we focus on the first case and present latest SSI research aimed at providing new alternative and augmentative communication methods for persons with severe speech disorders. SSIs can employ a variety of biosignals to enable silent communication, such as electrophysiological recordings of neural activity, electromyographic (EMG) recordings of vocal tract movements or the direct tracking of articulator movements using imaging techniques. Depending on the disorder, some sensing techniques may be better suited than others to capture speech-related information. For instance, EMG and imaging techniques are well suited for laryngectomised patients, whose vocal tract remains almost intact but are unable to speak after the removal of the vocal folds, but fail for severely paralysed individuals. From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms. Despite considerable advances in recent years, most present-day SSIs have only been validated in laboratory settings for healthy users. Thus, as discussed in this paper, a number of challenges remain to be addressed in future research before SSIs can be promoted to real-world applications. If these issues can be addressed successfully, future SSIs will improve the lives of persons with severe speech impairments by restoring their communication capabilities.

This paper describes a technique that generates speech acoustics from articulator movements. Our motivation is to help people who can no longer speak following laryngectomy, a procedure that is carried out tens of thousands of times per year in the Western world. Our method for sensing articulator movement, permanent magnetic articulography, relies on small, unobtrusive magnets attached to the lips and tongue. Changes in magnetic field caused by magnet movements are sensed and form the input to a process that is trained to estimate speech acoustics. In the experiments reported here this “Direct Synthesis” technique is developed for normal speakers, with glued-on magnets, allowing us to train with parallel sensor and acoustic data. We describe three machine learning techniques for this task, based on Gaussian mixture models, deep neural networks, and recurrent neural networks (RNNs). We evaluate our techniques with objective acoustic distortion measures and subjective listening tests over spoken sentences read from novels (the CMU Arctic corpus). Our results show that the best performing technique is a bidirectional RNN (BiRNN), which employs both past and future contexts to predict the acoustics from the sensor data. BiRNNs are not suitable for synthesis in real time but fixed-lag RNNs give similar results and, because they only look a little way into the future, overcome this problem. Listening tests show that the speech produced by this method has a natural quality that preserves the identity of the speaker. Furthermore, we obtain up to 92% intelligibility on the challenging CMU Arctic material. To our knowledge, these are the best results obtained for a silent-speech system without a restricted vocabulary and with an unobtrusive device that delivers audio in close to real time. This work promises to lead to a technology that truly will give people whose larynx has been removed their voices back.

This paper presents a recursive expectation-maximization algorithm for online multichannel speech enhancement. A deep neural network mask estimator is used to compute the speech presence probability, which is then improved by means of statistical spatial models of the noisy speech and noise signals. The clean speech signal is estimated using beamforming, single-channel linear postfiltering and speech presence masking. The clean speech statistics and speech presence probabilities are finally used to compute the acoustic parameters for beamforming and postfiltering by means of maximum likelihood estimation. This iterative procedure is carried out on a frame-by-frame basis. The algorithm integrates the different estimates in a common statistical framework suitable for online scenarios. Moreover, our method can successfully exploit spectral, spatial and temporal speech properties. Our proposed algorithm is tested in different noisy environments using the multichannel recordings of the CHiME-4 database. The experimental results show that our method outperforms other related state-of-the-art approaches in noise reduction performance, while allowing low-latency processing for real-time applications.

Network-based speech recognition (NSR) and distributed speech recognition (DSR) have been proposed as solutions to translate speech recognition technologies to mobile environments. NSR is the most straightforward solution since it does not require any modification in the mobile phone, however DSR offers higher robustness against codec compression and transmission channel degradation. This paper explores an alternative approach for remote speech recognition which combines the advantages of NSR and DSR. In this scheme, a standard speech codec is used for speech transmission but the recognition is performed from the received codec parameters. In particular, we focus on the effect of transmission channel errors, which can cause a more severe performance reduction on speech recognition than codec distortion. First, we show that an NSR solution can approach DSR through a reconstruction technique along with an adapted noise reduction technique originally proposed for acoustic noise. Then, these results are improved by working with recognition features directly extracted from the codec bitstream by means of parameter transcoding. Required modifications on current networks in order to access the bitstream are described. The network upgrading with the tandem free operation (TFO) protocol is an attractive solution. This upgrade not only offers an overall improvement on the end-to-end speech quality, but would also allow a recognition performance similar, and even higher in poor channel conditions, to that obtained by DSR when parameter transcoding along with the proposed mitigation techniques are applied

This paper addresses the problem of feature compensation in the log-spectral domain by using the missing-data (MD) approach to noise robust speech recognition, that is, the log-spectral features can be either almost unaffected by noise or completely masked by it. First, a general MD framework based on minimum mean square error (MMSE) estimation is introduced which exploits the correlation across frequency bands to reconstruct the missing features. This framework allows the derivation of different MD imputation approaches and, in particular, a novel technique taking advantage of truncated Gaussian distributions is presented. While the proposed technique provides excellent results at high and medium signal-to-noise ratios (SNRs), its performance diminishes at low SNRs where very few reliable features are available. The reconstruction technique is therefore extended to exploit temporal constraints using two different approaches. In the first approach, time-frequency patches of speech containing a number of consecutive frames are modeled using a Gaussian mixture model (GMM). In the second one, the sequential structure of speech is alternatively modeled by a hidden Markov model (HMM). The proposed techniques are evaluated on Aurora-2 and Aurora-4 databases using both oracle and estimated masks. In both cases, the proposed techniques outperform the recognition performance obtained by the baseline system and other related techniques. Also, the introduction of a temporal modeling turns out to be very effective in reconstructing spectra at low SNRs. In particular, HMMs show the highest capability of accounting for time correlations and, therefore, achieve the best results.