Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

The Use of Optimal Cue Mapping to Improve the Intelligibility and Quality of Speech in Complex Binaural Sound Mixtures.

A person with normal hearing has the ability to follow a particular conversation of interest in a noisy and reverberant environment, whilst simultaneously ignoring the interfering sounds. This task often becomes more challenging for individuals with a hearing impairment. Attending selectively to a sound source is difficult to replicate in machines, including devices such as hearing aids. A correctly set up hearing aid will work well in quiet conditions, but its performance may deteriorate seriously in the presence of competing sounds. To be of help in these more challenging situations the hearing aid should be able to segregate the desired sound source from any other, unwanted sounds. This thesis explores a novel approach to speech segregation based on optimal cue mapping (OCM). OCM is a signal processing method for segregating a sound source based on spatial and other cues extracted from the binaural mixture of sounds arriving at a listener's ears. The spectral energy fraction of the target speech source in the mixture is estimated frame-by-frame using artificial neural networks (ANNs). The resulting target speech magnitude estimates for the left and right channels are combined with the corresponding original phase spectra to produce the final binaural output signal. The performance improvements delivered by the OCM algorithm are evaluated using the STOI and PESQ metrics for speech intelligibility and quality, respectively. A variety of increasingly challenging binaural mixtures are synthesised involving up to five spatially separate sound sources in both anechoic and reverberant environments. The segregated speech consistently exhibits gains in intelligibility and quality and compares favourably with a leading, somewhat more complex approach. The OCM method allows the selection and integration of multiple cues to be optimised and provides scalable performance benefits to suit the available computational resources. The ability to determine the varying relative importance of each cue in different acoustic conditions is expected to facilitate computationally efficient solutions suitable for use in a hearing aid, allowing the aid to operate effectively in a range of typical acoustic environments. Further developments are proposed to achieve this overall goal

Investigating the build-up of precedence effect using reflection masking

The auditory processing level involved in the build‐up of precedence [Freyman et al., J. Acoust. Soc. Am. 90, 874–884 (1991)] has been investigated here by employing reflection masked threshold (RMT) techniques. Given that RMT techniques are generally assumed to address lower levels of the auditory signal processing, such an approach represents a bottom‐up approach to the buildup of precedence. Three conditioner configurations measuring a possible buildup of reflection suppression were compared to the baseline RMT for four reflection delays ranging from 2.5–15 ms. No buildup of reflection suppression was observed for any of the conditioner configurations. Buildup of template (decrease in RMT for two of the conditioners), on the other hand, was found to be delay dependent. For five of six listeners, with reflection delay=2.5 and 15 ms, RMT decreased relative to the baseline. For 5‐ and 10‐ms delay, no change in threshold was observed. It is concluded that the low‐level auditory processing involved in RMT is not sufficient to realize a buildup of reflection suppression. This confirms suggestions that higher level processing is involved in PE buildup. The observed enhancement of reflection detection (RMT) may contribute to active suppression at higher processing levels

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Single Channel auditory source separation with neural network

Although distinguishing different sounds in noisy environment is a relative easy task for human, source separation has long been extremely difficult in audio signal processing. The problem is challenging for three reasons: the large variety of sound type, the abundant mixing conditions and the unclear mechanism to distinguish sources, especially for similar sounds. In recent years, the neural network based methods achieved impressive successes in various problems, including the speech enhancement, where the task is to separate the clean speech out of the noise mixture. However, the current deep learning based source separator does not perform well on real recorded noisy speech, and more importantly, is not applicable in a more general source separation scenario such as overlapped speech. In this thesis, we firstly propose extensions for the current mask learning network, for the problem of speech enhancement, to fix the scale mismatch problem which is usually occurred in real recording audio. We solve this problem by combining two additional restoration layers in the existing mask learning network. We also proposed a residual learning architecture for the speech enhancement, further improving the network generalization under different recording conditions. We evaluate the proposed speech enhancement models on CHiME 3 data. Without retraining the acoustic model, the best bi-direction LSTM with residue connections yields 25.13% relative WER reduction on real data and 34.03% WER on simulated data. Then we propose a novel neural network based model called “deep clustering” for more general source separation tasks. We train a deep network to assign contrastive embedding vectors to each time-frequency region of the spectrogram in order to implicitly predict the segmentation labels of the target spectrogram from the input mixtures. This yields a deep network-based analogue to spectral clustering, in that the embeddings form a low-rank pairwise affinity matrix that approximates the ideal affinity matrix, while enabling much faster performance. At test time, the clustering step “decodes” the segmentation implicit in the embeddings by optimizing K-means with respect to the unknown assignments. Experiments on single channel mixtures from multiple speakers show that a speaker-independent model trained on two-speaker and three speakers mixtures can improve signal quality for mixtures of held-out speakers by an average over 10dB. We then propose an extension for deep clustering named “deep attractor” network that allows the system to perform efficient end-to-end training. In the proposed model, attractor points for each source are firstly created the acoustic signals which pull together the time-frequency bins corresponding to each source by finding the centroids of the sources in the embedding space, which are subsequently used to determine the similarity of each bin in the mixture to each source. The network is then trained to minimize the reconstruction error of each source by optimizing the embeddings. We showed that this frame work can achieve even better results. Lastly, we introduce two applications of the proposed models, in singing voice separation and the smart hearing aid device. For the former, a multi-task architecture is proposed, which combines the deep clustering and the classification based network. And a new state of the art separation result was achieved, where the signal to noise ratio was improved by 11.1dB on music and 7.9dB on singing voice. In the application of smart hearing aid device, we combine the neural decoding with the separation network. The system firstly decodes the user’s attention, which is further used to guide the separator for the targeting source. Both objective study and subjective study show the proposed system can accurately decode the attention and significantly improve the user experience

Analysis and resynthesis of polyphonic music

This thesis examines applications of Digital Signal Processing to the analysis, transformation, and resynthesis of musical audio. First I give an overview of the human perception of music. I then examine in detail the requirements for a system that can analyse, transcribe, process, and resynthesise monaural polyphonic music. I then describe and compare the possible hardware and software platforms. After this I describe a prototype hybrid system that attempts to carry out these tasks using a method based on additive synthesis. Next I present results from its application to a variety of musical examples, and critically assess its performance and limitations. I then address these issues in the design of a second system based on Gabor wavelets. I conclude by summarising the research and outlining suggestions for future developments