Signal Processing - July 2017 - 115
Table 1. Selected data-exploitation studies on the limited labeled speech resource. (continued )
Publications
Types
Approaches
Models
Applications
Databases and Languages
Hamanaka et al. 2010 [82]
AL
Query by committee
GMM-HMM
ASR
Corpus of Spontaneous Japanese (Ja)
Zhang and Schuller 2012 [83]
AL
Meta query
SVM
ER
FAU AEC (Ge)
Zhang et al. 2015 [84]
AL
Meta query
SVM
ER
FAU AEC (Ge)
Riccardi and Hakkani-Tür
2003 [85]
CL
Confidence score
HMM
ASR
"How May I Help You?" database (En)
Yu et al. 2010 [86]
CL
Confidence score
HMM
ASR
Broadcast Conv. and News corpora (Ma)
Zhang et al. 2015 [17]
CL
Confidence score
SVM
ER
FAU AEC (Ge), SUSAS (En)
Yu et al. 2010 [87]
CL
Global-entropy based
HMM
ASR
Directory assistance data (En)
BN: Bayes network; CTC: connectionist temporal classification; NB: naive Bayes; LR: logistic regression; RDT: randomized decision making; DAU: data augmentation; SS: speech
synthesis; ER: emotion recognition; As/Da/En/Fr/Ge/Ha/Ja/Ma/Sp/Tu/Vi/Xi/Zu: Assamese/Danish/English/French/German/Haitian Creole/Japanese/Mandarin/Spanish/
Turkish/Vietnamese/Xitsonga/Zulu.
where a, the wrapping factor, is randomly chosen from
[0.9, 1.1] . The results presented in [40] indicate that, in
terms of the phone error rate, deep networks trained on a
VTLP-augmented version of a small database can outperform the deep networks trained on the original data set.
Based on that work, a deterministic perturbation (i.e., a
changes in the range of warping factors with a fixed
step) rather than a random perturbation was proposed and
investigated [42].
SFM, inspired by voice conversion paradigms, seeks to
utilize the acoustic-feature-space relationship among speakers when augmenting a data set [41]. Specifically, it augments
training utterances by statistically converting one speaker's
speech data to another's using
x l = x · M,
(6)
where M is a transformation matrix of the feature spaces
between two speakers. The experimental results given in [41]
show that SFM offers improved performance over VTLP on
both ASR and keyword spotting (KWS) tasks.
Other data augmentation approaches include tempo-based,
speed-based, and volume-based perturbations [43]. Tempobased perturbation modifies the speech tempo while retaining
the pitch and the spectral envelope. Speed-based perturbation
varies the speech speed by resampling, whereas volume-based
perturbation changes the amplitude of signals.
While data augmentation approaches have frequently been
effective in ASR tasks [7], [44], this has not proved to be as
much the case in other ASA tasks, particularly in computational
paralinguistics [45]. A potential reason for this might be that the
detection of speaker states and traits (e.g., emotion, age, and gender) is more sensitive to changes in speech variation. Therefore,
training on inappropriately transformed speech would lead to a
worse model. Emotion, for example, is known to be related to the
speech tempo; speech with faster tempo is inclined to be recognized as higher arousal in emotion recognition, so changing the
associated speech tempo from fast to slow would potentially lead
to badly labeled training data.
Continued research efforts being undertaken to distinguish features that are task specific or task invariant could
help facilitate the application of data augmentation to other
speech analysis tasks. In addition, most recent applications of
data augmentation are performed for deep learning [7]. The
effectiveness of these techniques on shallow discriminative or
generative models is yet to be established.
Speech synthesis
Similar to data augmentation, the speech synthesis approach
aims to synthesize additional labeled data, i.e., L syn = SS (L),
such that the new labeled data set Ll is updated by
Ll = L , L syn . Theoretically, speech synthesis can produce an
infinite amount of labeled data via altering speech content or
modifying the parameters of a speech synthesizer. However, as
the parameters of the synthesizers have a limited range, the
simulated speech data often face the problem of limited variations. This can consequently result in the overfitting issue when
training models. Combining the synthesized speech data with
natural instances has been shown to help minimize this overfitting issue [46]. For emotion recognition in speech, it has been
shown that systems trained on synthesized speech (the test data
was natural speech) can deliver competitive performance when
compared to equivalent systems trained on natural speech [46].
In this article, two synthesizers rendering emotional speech-
Emofilt and Mbrola-were utilized to artificially generate
speech colored with predefined emotions [46].
Rather than directly synthesizing waveforms, an alternative
is generating parameterized speech that can be used directly
for training a discriminative classifier. Gales et al. [47] used
a hidden Markov model (HMM)-based statistical synthesis to
generate missing words in a training set, when building wordbased support vector machines (SVMs) for ASR. The results
presented indicate that this HMM-based synthesis approach
was able to yield gains over the baseline. Inspired by the success of deep learning, an emerging research trend is to use NNs
rather than HMMs to generate speech samples [48], which may
also mature in terms of the variation of synthesized speaker
states and traits.
IEEE SIGNAL PROCESSING MAGAZINE
|
July 2017
|
115
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