Data Mining Practical Machine Learning Tools and Techniques

http://cs.uef.fi/sipu/pub/SRE-review-Kinnunen-and-Li.pdf

An overview of text-independent speaker recognition: From features to supervectors

Work on voice sciences over recent decades has led to a proliferation of acoustic parameters that are used quite selectively and are not always extracted in a similar fashion. With many independent teams working in different research areas, shared standards become an essential safeguard to ensure compliance with state-of-the-art methods allowing appropriate comparison of results across studies and potential integration and combination of extraction and recognition systems. In this paper we propose a basic standard acoustic parameter set for various areas of automatic voice analysis, such as paralinguistic or clinical speech analysis. In contrast to a large brute-force parameter set, we present a minimalistic set of voice parameters here. These were selected based on a) their potential to index affective physiological changes in voice production, b) their proven value in former studies as well as their automatic extractability, and c) their theoretical significance. The set is intended to provide a common baseline for evaluation of future research and eliminate differences caused by varying parameter sets or even different implementations of the same parameters. Our implementation is publicly available with the openSMILE toolkit. Comparative evaluations of the proposed feature set and large baseline feature sets of INTERSPEECH challenges show a high performance of the proposed set in relation to its size.

/pdf/the-geneva-minimalistic-acoustic-parameter-set-gemaps-for-36spb4mksx.pdf

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech processing applications, especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related applications. This new area of machine learning has yielded far better results when compared to others in a variety of applications including speech, and thus became a very attractive area of research. This paper provides a thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 174 papers published between the years 2006 and 2018. The results provided in this paper shed light on the trends of research in this area as well as bring focus to new research topics.

/pdf/speech-recognition-using-deep-neural-networks-a-systematic-kp43ilkz5h.pdf

Speech Recognition Using Deep Neural Networks: A Systematic Review

The INTERSPEECH 2013 Computational Paralinguistics Challenge provides for the first time a unified test-bed for Social Signals such as laughter in speech. It further introduces conflict in group discussions as a new task and deals with autism and its manifestations in speech. Finally, emotion is revisited as task, albeit with a broader range of overall twelve enacted emotional states. In this paper, we describe these four Sub-Challenges, their conditions, baselines, and a new feature set by the openSMILE toolkit, provided to the participants. Index Terms: Computational Paralinguistics, Challenge, Social Signals, Conflict, Emotion, Autism

/pdf/the-interspeech-2013-computational-paralinguistics-challenge-2s3h1zujqy.pdf

The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism

We explore how intrinsic variations (those associated with the speaker rather than the recording environment) affect textindependent speaker verification performance. In a previous paper we introduced the SRI-FRTIV corpus and provided speaker verification results using a Gaussian mixture model (GMM) system on telephone-channel speech. In this paper we explore the use of other speaker verification systems on the telephone channel data and compare against the GMM baseline. We found the GMM system to be one of the more robust across all conditions. Systems relying on recognition hypotheses had a significant degradation in low vocal effort conditions. We also explore the use of the GMM system on several other channels. We found improved performance on table-top microphones compared to the telephone channel in furtive conditions and gradual degradations as a function of the distance from the microphone to the speaker. Therefore distant microphones further degrade the speaker verification performance due to intrinsic variability. Index Terms: speaker recognition, vocal effort, speaking style, intrinsic variation, furtive speech, interview speech, read speech, oration

/pdf/feature-based-and-channel-based-analyses-of-intrinsic-1hzvfiyldx.pdf

Feature-Based and Channel-Based Analyses of Intrinsic Variability in Speaker Verification

In this paper, we present an automatic classification approach to identify reading disorders in children This identification is based on a standardized test In the original setup the test is performed by a human supervisor who measures the reading duration and notes down all reading errors of the child at the same time In this manner we recorded tests of 38 children who were suspected to have reading disorders The data was confronted to an automatic system which employs speech recognition and prosodic analysis to identify the reading errors In a subsequent classification experiment -- based on the speech recognizer's output, the duration of the test, and prosodic features -- 947 % of the children could be classified correctly

/pdf/towards-the-automatic-classification-of-reading-disorders-in-191uc58qq9.pdf

Towards the Automatic Classification of Reading Disorders in Continuous Text Passages

Current ﬁndings show that pre-trained wav2vec 2.0 models can be successfully used as feature extractors to discriminate on speaker-based tasks. We demonstrate that latent representations extracted at different layers of a pre-trained wav2vec 2.0 system can be effec-tively used for binary classiﬁcation of various types of pathologic speech. We examine the pathologies laryngectomy, oral squamous cell carcinoma, parkinson’s disease and cleft lip and palate for this purpose. The results show that a distinction between pathological and healthy voices, especially with latent representations from the lower layers, performs well with the lowest accuracy from 77.2% for parkinson’s disease to 100% for laryngectomy classiﬁcation. However, cross-pathology and cross-healthy tests show that the trained classiﬁers seem to be biased. The recognition rates vary consider-ably if there is a mismatch between training and out-of-domain test data, e.g., in age, spoken content or acoustic conditions.

The Importance of Speech Stimuli for Pathologic Speech Classification

This work aims to automatically evaluate whether the language development of children is age-appropriate. Validated speech and language tests are used for this purpose to test the auditory memory. In this work, the task is to determine whether spoken nonwords have been uttered correctly. We compare different approaches that are motivated to model specific language structures: Low-level features (FFT), speaker embeddings (ECAPA- TDNN), grapheme-motivated embeddings (wav2vec 2.0), and phonetic embeddings in form of senones (ASR acoustic model). Each of the approaches provides input for VGG-like 5-layer CNN classifiers. We also examine the adaptation per non- word. The evaluation of the proposed systems was performed using recordings from different kindergartens of spoken non- words. ECAPA-TDNN and low-level FFT features do not ex-plicitly model phonetic information; wav2vec2.0 is trained on grapheme labels, our ASR acoustic model features contain (sub-)phonetic information. We found that the more granular the phonetic modeling is, the higher are the achieved recognition rates. The best system trained on ASR acoustic model features with VTLN achieved an accuracy of 89.4% and an area under the ROC (Receiver Operating Characteristic) curve (AUC) of 0.923. This corresponds to an improvement in accuracy of 20.2% and AUC of 0.309 relative compared to the FFT-baseline.

Nonwords Pronunciation Classification in Language Development Tests for Preschool Children

The invention relates to acoustic event detection based on modeling of sequence of event subparts. Techniques are provided for acoustic event detection. A methodology implementing the techniques according to an embodiment includes extracting acoustic features from a received audio signal. The acoustic features may include, for example, one or more short-term Fourier transform frames, or other spectral energy characteristics, of the audio signal. The method also includes applying a trained classifier to the extracted acoustic features to identify and label acoustic event subparts of the audio signal and to generate scores associated with the subparts. The method further includes performing sequence decoding of the acoustic event subparts and associated scores to detect target acoustic events of interest based on the scores and temporal ordering sequence of the event subparts. The classifier is trained on acoustic event subparts that are generated through unsupervised subspace clusteringtechniques applied to training data that includes target acoustic events.

Tobias Bocklet

Papers

Feature-Based and Channel-Based Analyses of Intrinsic Variability in Speaker Verification

Towards the Automatic Classification of Reading Disorders in Continuous Text Passages

The Importance of Speech Stimuli for Pathologic Speech Classification

Nonwords Pronunciation Classification in Language Development Tests for Preschool Children

Acoustic event detection based on modelling of sequence of event subparts