Distributed acoustic modeling with back-off n-grams

Chelba, Ciprian; Xu, Peng; Pereira, Fernando; Richardson, Thomas S.

doi:10.1109/icassp.2012.6288827

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…In [12], Ignacioet al used Deep Neural Networks (DNN), a state-of-art machine learning technique, to identify the language spoken by smartphone users. There are also a number of theoretical papers focusing on improve the computation e ciency of the training process of speech recognition [13][14][15]. These works are quite different from ours, since AccelWord relies on accelerometer to monitor voice signals and targets only on hotword detection.…”

Section: Related Workmentioning

confidence: 98%

“…Note that the other dominant factor in slow adaptation of voice control application is inaccuracy in speech recognition after the hotword is detected. However, since there is plethora of research [11][12][13][14][15] already done on this topic, we do not consider complete speech recognition in this work and simply focus on the hotword detection.…”

Section: Design Goals and Challengesmentioning

confidence: 99%

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AccelWord

Zhang

Pathak

et al. 2015

Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services

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Voice control has emerged as a popular method for interacting with smart-devices such as smartphones, smartwatches etc. Popular voice control applications like Siri and Google Now are already used by a large number of smartphone and tablet users. A major challenge in designing a voice control application is that it requires continuous monitoring of user's voice input through the microphone. Such applications utilize hotwords such as "Okay Google" or "Hi Galaxy" allowing them to distinguish user's voice command and her other conversations. A voice control application has to continuously listen for hotwords which significantly increases the energy consumption of the smart-devices.To address this energy e ciency problem of voice control, we present AccelWord in this paper. AccelWord is based on the empirical evidence that accelerometer sensors found in today's mobile devices are sensitive to user's voice. We also demonstrate that the e↵ect of user's voice on accelerometer data is rich enough so that it can be used to detect the hotwords spoken by the user. To achieve the goal of low energy cost but high detection accuracy, we combat multiple challenges, e.g. how to extract unique signatures of user's speaking hotwords only from accelerometer data and how to reduce the interference caused by user's mobility.We finally implement AccelWord as a standalone application running on Android devices. Comprehensive tests show AccelWord has hotword detection accuracy of 85% in static scenarios and 80% in mobile scenarios. Compared to the microphone based hotword detection applications such as Google Now and Samsung S Voice, AccelWord is 2 times more energy e cient while achieving the accuracy of 98% and 92% in static and mobile scenarios respectively.

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Section: Related Workmentioning

confidence: 98%

Section: Design Goals and Challengesmentioning

confidence: 99%

AccelWord

Zhang

Pathak

et al. 2015

Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services

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“…Discriminative language models (DLMs) [14] aim at directly optimizing word error rate by rewarding features that appear in low error hypotheses and penalizing features in misrecognized hypotheses. Since the estimation of discriminative LMs is computationally more intensive than regular n-gram LM one has to use distributed learning algorithms and supporting parallel computing infrastructure [16].…”

Section: Natural Language Predictionmentioning

confidence: 99%

Natural Language Understanding and Prediction: from Formal Grammars to Large Scale Machine Learning

Duta

2014

Fundamenta Informaticae

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Scientists have long dreamed of creating machines humans could interact with by voice. Although one no longer believes Turing's prophecy that machines will be able to converse like humans in the near future, real progress has been made in the voice and text-based human-machine interaction. This paper is a light introduction and survey of some deployed natural language systems and technologies and their historical evolution. We review two fundamental problems involving natural language: the language prediction problem and the language understanding problem. While describing in detail all these technologies is beyond our scope, we do comment on some aspects less discussed in the literature such as language prediction using huge models and semantic labeling using Marcus contextual grammars.

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“…Performances have been measured in terms of Precision at 1 (P@1), that is the percentage of correctly transcribed sentences occupying the first position in the rank, and Word Error Rate (or WER). All audio files are analyzed through the official Google ASR APIs [21]. In order to reduce the evaluation bias to ASR errors, only those commands with an available solution within the 5 input candidates were retained for the experiments.…”

Section: Experimental Evaluationsmentioning

confidence: 99%

Robust Spoken Language Understanding for House Service Robots

Vanzo¹,

Croce²,

Bastianelli³

et al. 2016

Polibits

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Abstract-Service robotics has been growing significantly in the last years, leading to several research results and to a number of consumer products. One of the essential features of these robotic platforms is represented by the ability of interacting with users through natural language. Spoken commands can be processed by a Spoken Language Understanding chain, in order to obtain the desired behavior of the robot. The entry point of such a process is represented by an Automatic Speech Recognition (ASR) module, that provides a list of transcriptions for a given spoken utterance. Although several well-performing ASR engines are available off-the-shelf, they operate in a general purpose setting. Hence, they may be not well suited in the recognition of utterances given to robots in specific domains. In this work, we propose a practical yet robust strategy to re-rank lists of transcriptions. This approach improves the quality of ASR systems in situated scenarios, i.e., the transcription of robotic commands. The proposed method relies upon evidences derived by a semantic grammar with semantic actions, designed to model typical commands expressed in scenarios that are specific to human service robotics. The outcomes obtained through an experimental evaluation show that the approach is able to effectively outperform the ASR baseline, obtained by selecting the first transcription suggested by the ASR.Index Terms-Spoken language understanding, service robotics, re-ranking of automatic speech recognition systems.

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Distributed acoustic modeling with back-off n-grams

Cited by 6 publications

References 6 publications

AccelWord

AccelWord

Natural Language Understanding and Prediction: from Formal Grammars to Large Scale Machine Learning

Robust Spoken Language Understanding for House Service Robots

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