HarkMan—A vocabulary-independent keyword spotter for spontaneous Chinese speech

Zheng, Fang; Xu, Mingxing; Mou, Xiaolong; Wu, Jian; Wu, Wenhu; Fang, Ditang

doi:10.1007/bf02952483

Cited by 3 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, most of the works are building voice activation systems for the English language, for which the MFCC and Log Mel-filterbank have proven themselves well. Researchers who used systems for other languages faced the necessity to use other features (Ida and Yamasaki, 1998;Zheng et al, 1999).…”

Section: Resultsmentioning

confidence: 99%

“…It turns out that this is not the case, for example, during the development of voice activation systems for the Japanese (Ida and Yamasaki, 1998), the prosodic information had to be used to achieve acceptable quality, as MFCC did not give sufficient results. This situation happens with some other languages, too (Zheng et al, 1999).…”

Section: Feature Representationmentioning

confidence: 96%

“…Note that it is the question of definitions what to consider an acoustic model in the HMM-GMM setup. You can either consider GMM (so the part which actually computes P (S|s), recall that the HMM state often represents some acoustic unit) or the whole HMM, because it expresses P (S|w) like in Zheng et al (1999) (w is a keyword).…”

Section: Acoustic Modelmentioning

confidence: 99%

“…Comparing to threshold (Morgan et al, 1990;Naylor et al, 1992;Junkawitsch et al, 1997;Keshet et al, 2009;Wöllmer et al, 2009b,a;Li and Wang, 2014;Chen et al, 2014a;Gruenstein et al, 2017;Benisty et al, 2018;Myer and Tomar, 2018) Viterby (Rose and Paul, 1990;Feng and Mazor, 1992;Wilcox and Bush, 1992;Rohlicek et al, 1993;Knill and Young, 1996;Junkawitsch et al, 1997;Zheng et al, 1999;Liu et al, 2000;Vasilache and Vasilache, 2009;Tabibian et al, 2011;Leow et al, 2012;Zhu et al, 2013;Kumatani et al, 2017;Ge and Yan, 2017;Sun et al, 2017) Forward-Backward algorithm (Wilcox and Bush, 1992;Rohlicek et al, 1993) DTW (Zeppenfeld and Waibel, 1992;Kosonocky and Mammone, 1995;Kurniawati et al, 2012;Zehetner et al, 2014;Hou et al, 2016) Likelihood ratio (Jansen and Niyogi, 2009c;Szöke et al, 2010) Fuzzy logic (Manor and Greenberg, 2017) Table 10 The metrics used in studied sources.…”

Section: Decoding Approach Sourcesmentioning

confidence: 99%

“…The history of voice activation models has gone through several important stages in parallel with solving a more general problem of automatic speech recognition. We would like to highlight the following important moments: the beginning of the use of hidden Markov models back in 1989 (Rohlicek et al, 1989), the use of neural networks since 1990 (Morgan et al, 1990Naylor et al, 1992), the use of pattern matching approaches, in particular, dynamic time wrapping (Zeppenfeld and Waibel, 1992) optimization of a loss functions specific to a voice activation (as opposed to the common metrics such as accuracy and similar; this enables the system to become more attractive in terms of user experience) (Chang and Lippmann, 1994;Szöke et al, 2010), attempts to get rid of a garbage model (Junkawitsch et al, 1997), building systems of voice activation for non-English languages such as Chinese (Zheng et al, 1999;Hao and Li, 2002), Japanese (Ida and Yamasaki, 1998), Persian (Shokri et al, 2011), construction of discriminative systems (Keshet et al, 2009;Tabibian et al, 2011Tabibian et al, , 2013, publications describing voice activation systems in mass products (Chen et al, 2014a;Gruenstein et al, 2017;Guo et al, 2018;Wu et al, 2018), as well as publishing open datasets to compare different approaches (Warden, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Voice Activation Systems for Embedded Devices: Systematic Literature Review

Kolesau¹,

Šešok²

2020

Informatica

View full text Add to dashboard Cite

A large number of modern mobile devices, embedded devices and smart home devices are equipped with a voice control. Automatic recognition of the entire audio stream, however, is undesirable for the reasons of the resource consumption and privacy. Therefore, most of these devices use a voice activation system, whose task is to find the specified in advance word or phrase in the audio stream (for example, Ok, Google) and to activate the voice request processing system when it is found. The voice activation system must have the following properties: high accuracy, ability to work entirely on the device (without using remote servers), consumption of a small amount of resources (primarily CPU and RAM), noise resistance and variability of speech, as well as a small delay between the pronunciation of the key phrase and the system activation. This work is a systematic literature review on voice activation systems that satisfy the above properties. We describe the principle of various voice activation systems' operation, the characteristic representation of sound in such systems, consider in detail the acoustic modelling and, finally, describe the approaches used to assess the models' quality. In addition, we point to a number of open questions in this problem.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Feature Representationmentioning

confidence: 96%

Section: Acoustic Modelmentioning

confidence: 99%

Section: Decoding Approach Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Voice Activation Systems for Embedded Devices: Systematic Literature Review

Kolesau¹,

Šešok²

2020

Informatica

View full text Add to dashboard Cite

show abstract

Improving the effectiveness of voice activation systems with machine learning methods

Kolesau¹

View full text Add to dashboard Cite

show abstract

Unsupervised Pre-Training for Voice Activation

Kolesau

Šešok

2020

Applied Sciences

View full text Add to dashboard Cite

The problem of voice activation is to find a pre-defined word in the audio stream. Solutions such as keyword spotter “Ok, Google” for Android devices or keyword spotter “Alexa” for Amazon devices use tens of thousands to millions of keyword examples in training. In this paper, we explore the possibility of using pre-trained audio features to build voice activation with a small number of keyword examples. The contribution of this article consists of two parts. First, we investigate the dependence of the quality of the voice activation system on the number of examples in training for English and Russian and show that the use of pre-trained audio features, such as wav2vec, increases the accuracy of the system by up to 10% if only seven examples are available for each keyword during training. At the same time, the benefits of such features become less and disappear as the dataset size increases. Secondly, we prepare and provide for general use a dataset for training and testing voice activation for the Lithuanian language. We also provide training results on this dataset.

show abstract

HarkMan—A vocabulary-independent keyword spotter for spontaneous Chinese speech

Cited by 3 publications

References 5 publications

Voice Activation Systems for Embedded Devices: Systematic Literature Review

Voice Activation Systems for Embedded Devices: Systematic Literature Review

Improving the effectiveness of voice activation systems with machine learning methods

Unsupervised Pre-Training for Voice Activation

Contact Info

Product

Resources

About