Data Mining Practical Machine Learning Tools and Techniques

From the Publisher:
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

/pdf/speech-and-language-processing-an-introduction-to-natural-atzaecqfem.pdf

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

http://www.ffri.hr/~ibrdar/komunikacija/seminari/Ververidis,%202006%20-%20Emotional%20speech%20recognition.pdf

Emotional speech recognition: Resources, features, and methods

http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2011/Schuller11-RRE.pdf

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Emotion recognition from speech has emerged as an important research area in the recent past. In this regard, review of existing work on emotional speech processing is useful for carrying out further research. In this paper, the recent literature on speech emotion recognition has been presented considering the issues related to emotional speech corpora, different types of speech features and models used for recognition of emotions from speech. Thirty two representative speech databases are reviewed in this work from point of view of their language, number of speakers, number of emotions, and purpose of collection. The issues related to emotional speech databases used in emotional speech recognition are also briefly discussed. Literature on different features used in the task of emotion recognition from speech is presented. The importance of choosing different classification models has been discussed along with the review. The important issues to be considered for further emotion recognition research in general and in specific to the Indian context have been highlighted where ever necessary.

/pdf/emotion-recognition-from-speech-a-review-2bhwxdu8ml.pdf

Emotion recognition from speech: a review

/pdf/proc-blizzard-challenge-2013-594z59iwcx.pdf

Proc. Blizzard Challenge 2013

Intent recognition is a key component of any task-oriented conversational system. The intent recognizer can be used first to classify the user’s utterance into one of several predefined classes (intents) that help to understand the user’s current goal. Then, the most adequate response can be provided accordingly. Intent recognizers also often appear as a form of joint models for performing the natural language understanding and dialog management tasks together as a single process, thus simplifying the set of problems that a conversational system must solve. This happens to be especially true for frequently asked question (FAQ) conversational systems. In this work, we first present an exploratory analysis in which different deep learning (DL) models for intent detection and classification were evaluated. In particular, we experimentally compare and analyze conventional recurrent neural networks (RNN) and state-of-the-art transformer models. Our experiments confirmed that best performance is achieved by using transformers. Specifically, best performance was achieved by fine-tuning the so-called BETO model (a Spanish pretrained bidirectional encoder representations from transformers (BERT) model from the Universidad de Chile) in our intent detection task. Then, as the main contribution of the paper, we analyze the effect of inserting unseen domain words to extend the vocabulary of the model as part of the fine-tuning or domain-adaptation process. Particularly, a very simple word frequency cut-off strategy is experimentally shown to be a suitable method for driving the vocabulary learning decisions over unseen words. The results of our analysis show that the proposed method helps to effectively extend the original vocabulary of the pretrained models. We validated our approach with a selection of the corpus acquired with the Hispabot-Covid19 system obtaining satisfactory results.

/pdf/fine-tuning-bert-models-for-intent-recognition-using-a-222cwafk.pdf

Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension

We present a multi-speaker formant synthesizer based on parameter concatenation. The user can choose among three speakers, two males and one female. The synthesizer stores all the parameters for the basic speaker and linear transformation functions to synthesized the other two. The complete database for one speaker consists of 455 parameterized units (diphones, triphones,...) and the parameters used are pitch, formants and bandwidths and source parameters (four parameters for the LF model, and glottal noise). To get the converted speaker we store a linear transformation function for each spectral stable segment of each unit. Preliminary results show that the quality of the synthesizer is very good and that this system can help us to study and understand the speaker variability problem.

/pdf/a-new-multi-speaker-formant-synthesizer-that-applies-voice-12kkg26ifx.pdf

A new Multi-Speaker Formant Synthesizer that applies Voice Conversion Techniques

In this paper, we study the approach followed to use the VoiceXML standard in a dialog system platform already available in our group. As VoiceXML interpreter we have chosen OpenVXI, an open source portable solution where we can make the modifications needed to adapt the solution to the characteristics of our recognition and synthesis modules; so we will emphasize the changes that we have had to make in such interpreter. Besides, we review some relevant modules in our platform and their capabilities, highlighting the use of standards in them, as SSML for the text-to-speech system and JSGF for the specification of grammars for recognition. Finally, we discuss several ideas regarding the limitations detected in VoiceXML.

/pdf/implementation-of-dialog-applications-in-an-open-source-4jxqk7xej1.pdf

Implementation of dialog applications in an open-source voiceXML platform.

This paper presents new advances in speech technology carried out by the Speech Technology Group (GTH) at the Universidad Politecnica de Madrid (iJPM) to develop enhanced interfaces at home. These interfaces provide a better interaction for people with disabilities. The speech recognizer includes a speaker identification feature (that makes an acoustic adaptation possible for improving recognition performance) and an emotion classifier (to detect the user emotion). The understanding module, with a bottom-up strategy, increases its flexibility against recognition errors. The dialog manager has been improved by a new dialog control based on Bayesian Networks and a new platform for developing multimodal, multilingual, and user dependent dialog services from scratch. Finally, the speech synthesis module includes new advances for increasing the voice naturalness and incorporating emotions. These advances have been integrated into a new interface for controlling a Hi-Fi audio system, thus significantly...

Juan Manuel Montero

Papers

Proc. Blizzard Challenge 2013

Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension

A new Multi-Speaker Formant Synthesizer that applies Voice Conversion Techniques

Implementation of dialog applications in an open-source voiceXML platform.

Speech technology at home: enhanced interfaces for people with disabilities