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

A smart home controller that responds to natural language input is demonstrated on an Intel® embedded processor. This device contains two DSP cores and a neural network co-processor which share 4MB SRAM. An embedded configuration of the Intel® RealSpeechTM speech recognizer and intent extraction engine runs on the DSP cores with neural network operations offloaded to the co-processor. The prototype demonstrates that continuous speech recognition and understanding is possible on hardware with very low power consumption. As an example application, control of lights in a home via natural language is shown. An Intel® development kit is demonstrated together with a set of tools. Conference attendees are encouraged to interact with the demo and development system.

Speech Recognition and Understanding on Hardware-Accelerated DSP.

In this paper, we describe a new Java framework for an easy and efficient way of developing new GUI based speech processing applications. Standard components are provided to display the speech signal, the power plot, and the spectrogram. Furthermore, a component to create a new transcription and to display and manipulate an existing transcription is provided, as well as a component to display and manually correct external pitch values. These Swing components can be easily embedded into own Java programs. They can be synchronized to display the same region of the speech file. The object-oriented design provides base classes for rapid development of own components.

/pdf/java-visual-speech-components-for-rapid-application-5fl2qb478b.pdf

Java Visual Speech Components for Rapid Application Development of GUI Based Speech Processing Applications.

Techniques related to speaker recognition are discussed. Such techniques include determining context aware confidence values formed of false accept and false reject rates determined by using adaptively updated acoustic environment score distributions matched to current score distributions.

Method and system of speaker recognition using context aware confidence modeling

This paper focuses on the automatic detection of a person’s blood level alcohol based on automatic speech processing approaches. We compare 5 different feature types with different ways of modeling. Experiments are based on the ALC corpus of IS2011 Speaker State Challenge. The classification task is restricted to the detection of a blood alcohol level above 0:5 ‰. Three feature sets are based on spectral observations: MFCCs, PLPs, TRAPS. These are modeled by GMMs. Classification is either done by a Gaussian classifier or by SVMs. In the later case classification is based on GMM-based supervectors, i.e. concatenation of GMM mean vectors. A prosodic system extracts a 292-dimensional feature vector based on a voicedunvoiced decision. A transcription-based system makes use of text transcriptions related to phoneme durations and textual structure. We compare the stand-alone performances of these systems and combine them on score level by logistic regression. The best stand-alone performance is the transcriptionbased system which outperforms the baseline by 4.8 % on the development set. A Combination on score level gave a huge boost when the spectral-based systems were added (73.6 %). This is a relative improvement of 12.7 % to the baseline. On the test-set we achieved an UA of 68.6 % which is a significant improvement of 4.1 % to the baseline system. Index Terms: GMM, alcohol intoxication, system fusion

/pdf/drink-and-speak-on-the-automatic-classification-of-alcohol-4dxqvq5lab.pdf

Tobias Bocklet

Papers

Speech Recognition and Understanding on Hardware-Accelerated DSP.

Java Visual Speech Components for Rapid Application Development of GUI Based Speech Processing Applications.

Method and system of speaker recognition using context aware confidence modeling

Drink and Speak: On the Automatic Classification of Alcohol Intoxication by Acoustic, Prosodic and Text-Based Features.

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on