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E-raamat: Extraction of Prosody for Automatic Speaker, Language, Emotion and Speech Recognition

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This updated book expands upon prosody for recognition applications of speech processing. It includes importance of prosody for speech processing applications; builds on why prosody needs to be incorporated in speech processing applications; and presents  methods for extraction and representation of prosody for applications such as  speaker recognition, language recognition and speech recognition. The updated book also includes information on the significance of prosody for emotion recognition and various prosody-based approaches for automatic emotion recognition from speech.

1 Significance of Prosody for Speaker, Language, Emotion, and Speech Recognition
1(22)
1.1 Introduction
1(1)
1.2 What is Prosody?
2(2)
1.2.1 Intonation
2(1)
1.2.2 Stress
3(1)
1.2.3 Rhythm
4(1)
1.3 Probabilistic Formulation of Recognition
4(1)
1.4 Significance of Prosody for Robust Recognition
5(2)
1.5 Automatic Speaker Recognition
7(3)
1.5.1 Speaker Recognition by Humans
7(1)
1.5.2 Speaker-Specific Aspect of Speech
8(1)
1.5.3 Significance of Prosody for Automatic Speaker Recognition
9(1)
1.6 Automatic Language Recognition
10(5)
1.6.1 Language Recognition by Humans
11(1)
1.6.2 Language-Specific Aspect of Speech
11(1)
1.6.3 Significance of Prosody for Automatic Language Recognition
12(3)
1.7 Automatic Emotion Recognition
15(5)
1.7.1 Emotion Recognition by Humans
15(1)
1.7.2 Emotion-Specific Aspect of Speech
16(2)
1.7.3 Significance of Prosody for Automatic Emotion Recognition
18(2)
1.8 Automatic Speech Recognition
20(2)
1.8.1 Speech Recognition by Humans
21(1)
1.8.2 Significance of Prosody for Automatic Speech Recognition
22(1)
1.9 Summary
22(1)
2 Extraction and Representation of Prosody for Speaker, Language, Emotion, and Speech Recognition
23(22)
2.1 Introduction
23(1)
2.2 ASR-Free Approaches for Automatic Segmentation and Representation of Prosody
24(15)
2.2.1 Syllable-Like Segmentation Using Location of Vowel Onset Points
24(6)
2.2.2 Syllable-Like Segmentation Using Information from F0 and Energy Contour
30(2)
2.2.3 Syllable-Like Segmentation Using Detection of Vowel Region
32(1)
2.2.4 Segmentation Using Inflections or Start/End of Voicing
33(1)
2.2.5 Segmentation as Pseudo Syllables
34(1)
2.2.6 Segmentation at Predefined Intervals
35(2)
2.2.7 Suprasegmental Parameterization
37(1)
2.2.8 Segmentation at Sentence/Phrase and Syllable Level
37(2)
2.3 ASR-Based Approaches for Extraction and Representation of Prosody
39(4)
2.3.1 Segmentation into Nonuniform Extraction Regions
39(3)
2.3.2 Segmentation into Pseudo Syllables
42(1)
2.4 Summary
43(2)
3 Modeling and Fusion of Prosody for Speaker, Language, Emotion, and Speech Recognition
45(12)
3.1 Introduction
45(1)
3.2 Modeling of Prosody
45(1)
3.3 Speaker Recognition Systems Based on Prosody
46(2)
3.4 Language Recognition Systems Based on Prosody
48(2)
3.5 Emotion Recognition Systems Based on Prosody
50(2)
3.6 Speech Recognition Systems Based on Prosody
52(1)
3.7 Fusion of Prosodic Evidence into the Conventional Recognition Applications
53(3)
3.8 Summary
56(1)
References 57
Dr. Leena Mary is with Department of Technical Education, Government of Kerala, India. Currently she works as  a Professor at Department of Electronics and Communication Engineering, Government Engineering College Idukki. Her Research interests include speech processing, image processing  and machine learning.  She is the author of Extraction and Representation of Prosody for Speaker, Speech and Language Recognition (Springer 2012).
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