Our application requires a keyword spotting system with a small memory footprint, low computational cost, and high precision To meet these requirements, we propose a simple approach based on deep neural networks A deep neural network is trained to directly predict the keyword(s) or subword units of the keyword(s) followed by a posterior handling method producing a final confidence score Keyword recognition results achieve 45% relative improvement with respect to a competitive Hidden Markov Model-based system, while performance in the presence of babble noise shows 39% relative improvement

/pdf/small-footprint-keyword-spotting-using-deep-neural-networks-148iaz4nzy.pdf

Small-footprint keyword spotting using deep neural networks

A speaker-independent hidden Markov model (HMM) keyword recognizer (KWR) based on a continuous-speech-recognition model is presented. The baseline keyword recognition system is described, and techniques for dealing with nonkeyword speech and linear channel effects are discussed. The training of acoustic models to provide an explicit representation of nonvocabulary speech is investigated. A likelihood ratio scoring procedure is used to account for sources of variability affecting keyword likelihood scores. An acoustic class-dependent spectral normalization procedure is used to provide explicit compensation for linear channel effects. Keyword recognition results for a standard conversational speech task with a 20-keyword vocabulary reach 82% probability of detection at a false alarm rate of 12 false alarms per keyword per hour. >

A hidden Markov model based keyword recognition system

http://groups.csail.mit.edu/sls/publications/2003/glass.csl2003.pdf

A probabilistic framework for segment-based speech recognition

Despite the rapid adoption of Voice over IP (VoIP), its security implications are not yet fully understood. Since VoIP calls may traverse untrusted networks, packets should be encrypted to ensure confidentiality. However, we show that when the audio is encoded using variable bit rate codecs, the lengths of encrypted VoIP packets can be used to identify the phrases spoken within a call. Our results indicate that a passive observer can identify phrases from a standard speech corpus within encrypted calls with an average accuracy of 50%, and with accuracy greater than 90% for some phrases. Clearly, such an attack calls into question the efficacy of current VoIP encryption standards. In addition, we examine the impact of various features of the underlying audio on our performance and discuss methods for mitigation.

/pdf/spot-me-if-you-can-uncovering-spoken-phrases-in-encrypted-dmysdm7f1z.pdf

Spot Me if You Can: Uncovering Spoken Phrases in Encrypted VoIP Conversations

A technique for wordspotting based on hidden Markov models (HMM's). The technique allows a speaker to specify keywords dynamically and to train the associated HMM's via a single repetition of a keyword. Non-keyword speech is modeled using an HMM trained from a prerecorded sample of continuous speech. The wordspotter is intended for interactive applications, such as the editing of voice mail or mixed-media documents, and for keyword indexing in single-speaker audio or video recordings.

Wordspotting for voice editing and indexing

A word-spotting system using Gaussian hidden Markov models is presented. Several aspects of this problem are investigated. Specifically, results are reported on the use of various signal processing and feature transformation techniques. The authors have observed that performance can be greatly affected by the choice of features used, the covariance structure of the Gaussian models, and transformations based on energy and feature distributions. Due to the open-set nature of the problem, the specific techniques for modeling out-of-vocabulary speech and the choice of scoring metric can have a significant effect on performance. >

Continuous hidden Markov modeling for speaker-independent word spotting

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Continuous hidden Markov modeling for speaker-independent word spotting