Julia’s Little Helper
:
A Real-time Demo of Cantonese/Mandarin
Emotional Speech Detection
William Y. Wang
Computer Science
Suzanne Yuen
Mechanical Engineering
CS 6998
Computational Approach to Emotional Speech
Instructor: Prof. Julia Hirschberg
Columbia University 12/21/2009
Review
1. Target Languages: Cantonese (9 tones) , Mandarin (4 tones)
2. Target Emotions: Anger and Gladness
3. Lexical Features: ASR using a HMM acoustic model trained on
Mandarin Broadcast News [1] and a simple hand-written decoding
dictionary.
4. Prosodic Features: Energy and Tonal Features
5. Real-time drawing of pitch contour, waveform and energy.
6. A text-to-speech agent to greet and teach user how to use this demo.
[1] Yang Shao, Lan Wang, E-Seminar: an Audio-guide e-Learning System, IEEE International Workshop
on Education Technology and Training (ETT) 2008.
Lexical Scoring 1-3pts
Energy 1 pt
Tone 1 pt
Dictionary of Affects in Language
by Dr. Cynthia Whissell
Words
Pleasantness
Activeness
Imagery
affect
1.7500
1.8571
1.6
affection
2.7778
2.2500
2.0
success
2.8571
1.8000
1.4
successes
3.0000
2.0000
1.4
Total words: 8742 words were included.
Source: It was actually developed using various sources, for
example, college student essays, interviews and teenagers
description of their own emotion state. So, it can have a broad
coverage and avoid biased data.
Sentence Lexical Scoring
“I won best paper award!”
Words
Pleasantness
I
2.3750
won
2.5556
Best
2.5455
Paper
1.2857
Award
2.8333
Score = (2.375 + 2.5556 + 2.5455 + 1.2857 + 2.8333) / 5 = 2.319
Machine Translation
English Word
Pleasantness
Chinese Translation
alcohol
1.7143
酒精(alcohol)
alcoholic
1.0000
酒精 (alcohol)
muscles
2.3333
肌肉(muscles)
muscular
2.6250
肌肉(muscles)
Multilingual Challenges: English  Chinese
Encoding and Mapping
Mandarin
Word
Pinyin
(Phone Set)
Corresponding
Cantonese
Pinyin
Mapping
喜欢(like)
xi3 hua1n
喜欢(like)
hei2 fu1n
睡觉(sleep)
shui4 jiao4
训觉(sleep)
fen4 gao4
Tasks:
1. Mandarin  Pinyin (Phone set used by Acoustic Model)
2. Mandarin  Cantonese
Note that not all words in Mandarin have theirs’ exact
and direct mappings in Cantonese words and vice versa.
3. Cantonese  Pinyin
Text-to-speech Engine
1. Implement the text-to-speech engine.
2. “Play with” a text-to-speech engine.
3. Engine: TruVoice
Lernout & Hauspie Speech Products, or L&H
Went bankrupt in 2001
technology now owned by Nuance
L&H TTS Functionality
• Developed in 1997
• Advanced text pre-processing and no vocabulary restrictions
• User-definable pronunciation dictionary
• Accurately pronounces surnames and place names
• Flexible pitch, volume and speech rate
• Intonation support for punctuation
Test Overview
1. Participants –
1. gender: 6 male, 6 female
2. Native Language – 6 Mandarin, 6 Cantonese
2. Two Parts
1. JLH module and self-rating (24 lines total)
2. Perception test – Rating lines from others (72)
Sentences
1. Three types – questions, exclamations, statements
2. Randomized order of sentences for each participant
3. Examples:
In Lexicon
Not In Lexicon
Angry & Not Angry
Insane!
痴(chi1) 线(xian4)！
You're wrong.
你(ni3) 错(cuo4) 了
(le,liao3,liao4) 。
Glad & Not Glad
When's the wedding?
什(shen2,shi2) 么
(ma,me) 时(shi2) 候
(hou4) 结(jie1,jie2) 婚
(hun1) ？
It's bubbly?
有(you3,you4) 泡
(pao1,pao4) 泡
(pao1,pao4) 吗
(ma2,ma3,ma) ?
Analysis
1. Plan to examine differences and affects of following:
1. Ratings - JLH star rating, self rating, & 3 perception ratings
2. Language – Cantonese, Mandarin
3. Gender – female, male
4. Sentence structure – exclamation, question, & statement
2. Interesting points –
1. Huge range of Chinese accents
2. Tones of words may change depending on previous words
(such as English a mug vs. an umbrella)
3. Variations in colloquial speech, addressed by using Chinese
script
Future Work
1. Improve the prosodic analysis. More features should be explored.
2. Improve the lexical scoring. Use POS tagger or other NLP tools to
weigh different constituents of recognized sentence.
3. Finer-grain the emotion types and investigate the differences.
4. Study translational divergence in English-Chinese MT .
Demo