Detecting keywords in Persian conversational telephony speech using a discriminative English keyword spotter

Shokri, Akram; Davarpour, Mohammad Hossein; Akbari, Ahmad; Nasersharif, Babak

doi:10.1109/isspit.2013.6781892

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…FOM (Gish et al, 1990;Rose and Paul, 1990;Naylor et al, 1992;Zeppenfeld and Waibel, 1992;Chang and Lippmann, 1994;Gish and Ng, 1993;Rohlicek et al, 1993;Knill and Young, 1996;Junkawitsch et al, 1997;Zheng et al, 1999;Szöke et al, 2005;Lehtonen, 2005;Jansen and Niyogi, 2009a,c;Szöke et al, 2010;Tabibian et al, 2011;Bohac, 2012;Sangeetha and Jothilakshmi, 2014;Sadhu and Ghosh, 2017;Tabibian et al, 2018) EER (Szöke et al, 2010;Bohac, 2012) Accuracy (Morgan et al, 1990Ida and Yamasaki, 1998;Ge and Yan, 2017;Benisty et al, 2018;Fernández-Marqués et al, 2018) FA/kw/h (Rohlicek et al, 1989;Vroomen and Normandin, 1992;Feng and Mazor, 1992;Leow et al, 2012;Kavya and Karjigi, 2014) ROC (Marcus, 1992;Siu et al, 1994;Keshet et al, 2009;Wöllmer et al, 2009bWöllmer et al, , 2013Shokri et al, 2013;Sadhu and Ghosh, 2017;Kumatani et al, 2017) Detection rate (Feng and Mazor, 1992;Khne et al, 2004;…”

Section: Metrics Sourcesmentioning

confidence: 99%

“…Voice activation systems can be applied in various areas: telephony (Shokri et al, 2013;Szöke et al, 2010), crime analysis (Kavya and Karjigi, 2014), the assistance systems in emergency situations (Zhu et al, 2013), automated management of airports (Tabibian, 2017) and, naturally, personal voice assistants, built-in mobile phones and home devices (Gruenstein et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Voice Activation Systems for Embedded Devices: Systematic Literature Review

Kolesau¹,

Šešok²

2020

Informatica

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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.

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Section: Metrics Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Voice Activation Systems for Embedded Devices: Systematic Literature Review

Kolesau¹,

Šešok²

2020

Informatica

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“…We already have evaluated the robustness of discKWS in noisy conditions and have shown that this discriminative keyword spotter is more robust to noise, in comparison with a conventional HMM-based keyword spotter [8]. We also have reported the robustness to language change of discKWS [9]. In this paper, we compare discKWS with a conventional HMM-based spotter from another aspect which is their ability to detect keywords and estimate the true place of their occurrence.…”

Section: Introductionmentioning

confidence: 99%

Improving keyword detection rate using a set of rules to merge HMM-based and SVM-based keyword spotting results

Shokri¹,

Davarpour

Akbari

2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Evaluating the accuracy of HMM-based and SVM-based spotters in detecting keywords and recognizing the true place of keyword occurrence shows that the HMM-based spotter detects the place of occurrence more precisely than the SVM-based spotter. On the other hand, the SVM-based spotter performs much better in detecting keywords and has higher detection rate.In this paper, we propose a rule based combination method for combining output of these two keyword spotters in order to benefit from features and advantages of each method and overcome weaknesses and drawbacks of them. Experimental results of applying this combination method on both clean and noisy test sets show that its recognition rate has considerable growth rather than each individual method.

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“…Voice activation systems find applications in various areas: telephony [15], speech spoofing detection [16,17] crime analysis [18], the assistance systems in emergency situations [19], automated management of airports [20], and, naturally, personal voice assistants, built-in in mobile phones and home devices [11].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Pre-Training for Voice Activation

Kolesau

Šešok

2020

Applied Sciences

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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.

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Detecting keywords in Persian conversational telephony speech using a discriminative English keyword spotter

Cited by 4 publications

References 15 publications

Voice Activation Systems for Embedded Devices: Systematic Literature Review

Voice Activation Systems for Embedded Devices: Systematic Literature Review

Improving keyword detection rate using a set of rules to merge HMM-based and SVM-based keyword spotting results

Unsupervised Pre-Training for Voice Activation

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