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Mandarin Chinese
Speech Recognition
Mandarin Chinese
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Tonal language (inflection matters!)
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Monosyllabic language
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1st tone – High, constant pitch (Like saying “aaah”)
2nd tone – Rising pitch (“Huh?”)
3rd tone – Low pitch (“ugh”)
4th tone – High pitch with a rapid descent (“No!”)
“5th tone” – Neutral used for de-emphasized syllables
Each character represents a single base syllable and tone
Most words consist of 1, 2, or 4 characters
Heavily contextual language
Mandarin Chinese and Speech
Processing
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Accoustic representations of Chinese
syllables
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Structural Form
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(consonant) + vowel + (consonant)
Mandarin Chinese and Speech
Processing
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Phone Sets
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Initial/final phones [1]
e.g. Shi, ge, zi = (shi + ib), (ge + e), (z + if)
 Initial phones: unvoiced
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1 phone
Final phones: voiced (tone 1-5)
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Can consist of multiple phones
Mandarin Chinese and Speech
Processing
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Strong tonal recognition is crucial to
distinguish between homonyms [3]
(especially w/o context)
Creating tone models is difficult
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Discontinuities exist in the F0 contour
between voiced and unvoiced regions
Prosody
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Prosody: “the rhythmic and intonational
aspect of language” [2]
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Embedded Tone Modeling[4]
Explicit Tone Modeling[4]
Tone Modeling
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Embedded Tone Modeling
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Tonal acoustic units are joined with spectral
features at each frame [4]
Explicit Tone Modeling
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Tone recognition is completed independently
and combined after post-processing [4]
Tone Modeling
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Pitch, energy, and duration (Prosody) combined
with lexical and syntactic features improves
tonal labeling
Coarticulation
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Variations in syllables can cause variations in tone:
Bu4 + Dui4 = Bu2 Dui4 (wrong)
Ni3 + Hao3 = Ni2 Hao3 (hello)
Emebedded Tone Modeling:
Two Stream Modeling
Ni, Liu, Xu
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Spectral Stream –MFCC’s (Mel frequency cepstral
coefficients)
 Describe vocal tract information
 Distinctive for phones (short time duration)
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Pitch/Tone Stream – requires smoothing
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Describe vibrations of the vocal chords
Independent of Spectral features
d/dt(pitch) aka tone and d2/dt2(pitch) are added
Embedded in an entire syllable
Affected by coarticulation (requires a longer time
window) – i.e. Sandhi Tone – context dependency
Embedded Tone Modeling:
Two Stream Modeling [4]
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Tonal Identification Features
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F0
Energy
Duration
Coarticulation (cont. speech)
Initially use 2 stream embedded model followed
by explicit modeling during lattice rescoring
(alignment?)
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Explicit tone modeling uses max. entropy framework
[4] (discriminative model)
Explicit Tone Modeling [4]
No.
1
Feature Description
Duration of current, previous, and following
syllables
# of Features
3
2
3
Previous syllable is or is not sp
4
Statistical Parameters of pitch and log-energy of
current syllable (i.e. max, min, mean, etc.)
10
5
Normalized max and mean of pitch and energy in
each syllable in the context window
12
6
7
Location of current syllable within word
Slope and intercept of F0 contour of current
syllable, its delta, and delta-delta
Tones of preceding and proceding syllables
1
6
1
2
Other Work
Chang, Zhou, Di, Huang, & Lee [1]
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3 Methods
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Powerful Language Model (no tone modeling)
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Embedded 2 Stream
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CER = 7.32%
Tone Stream + Feature Stream
CER = 6.43%
Embedded 1 Stream
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Developed Pitch extractor
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pitch track added to feature vector
CER = 6.03%
Other Work
Qian, Soong [3]
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F0 contour smoothing
Multi-Space Distribution (MSD)
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Models 2 prob. Spaces
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Unvoiced: Discrete
Voiced (F0 Contour): Continuous
Other Work
Lamel, Gauvain, Le, Oparin, Meng [6]
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Multi-Layer Perceptron Features
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Compare Language Models
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Combined with MFCC’s and Pitch features
N-Gram: Back-off Language Model
Neural Network Language Model
Language Model Adaptation
Other Work
O. Kalinli [7]
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Replace prosodic features with biologically inspired
auditory attention cues
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Cochlear filtering, inner hair cell, etc.
Other features are extracted from the auditory
spectrum
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Intensity
Frequency contrast
Temporal contrast
Orientation (phase)
Other Work
Qian, Xu, Soong [8]
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Cross-Lingual Voice Transformation
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Phonetic mapping between languages
Difficult for Mandarin and English
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Very different prosodic features
References
[1] Eric Chang, Jianlai Zhou, Shuo Di, Chao Huang, & Kai-fu Li, “Large Vocabulary
Mandarin Speech Recognition with different Approached in Modeling Tones”
[2] Meriam-Webster Dictionary, http://www.merriam-webster.com/
[3] Yao Qian & Frank Soong, “A Multispace Distribution (MSD) and Two Stream Tone
Modeling Approach to Mandarin Speech Recognition”, Science Direct, 2009
[4]Chongjia Ni, Wenju Liu, & Bo Xu, “Improved Large vocabulary Mandarin Speech
Recognition using Prosodic and Lexical Information in Maximum Entropy Framework”
[5] Yi Liu & Pascale Fung, “Pronunciation Modeling for Spontaneous Mandarin Speech
Recognition”, International Journal of Speech Technology, 2004
[6] Lori Lamel, J.L. Gauvain, V.B. Le, I. Oparin, S. Meng, “Improved Models For Mandarin
Speech to Text Transcription, ICASSP, 2011
[7] O. Kalinli, “Tone and Pitch Accent Classification Using Auditory Attention Cues”,
ICASSP, 2011
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