Query-by-example spoken term detection using phonetic posteriorgram templates

Hazen, Timothy J.; Shen, Wade; White, Christopher M.

doi:10.1109/asru.2009.5372889

Cited by 242 publications

(175 citation statements)

References 9 publications

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“…Like the phonetic posteriorgrams used in [11,12], a supervised or semi-supervised DBM posteriorgram is a probability vector representing the posterior probabilities of a set of labeled phonetic units for a speech frame. Formally, if we denotes N speech frames as x 1 , .…”

Section: Semi-supervised Dbm Posteriorgrammentioning

confidence: 99%

Resource configurable spoken query detection using Deep Boltzmann Machines

Zhang¹,

Salakhutdinov

Chang³

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper we present a spoken query detection method based on posteriorgrams generated from Deep Boltzmann Machines (DBMs). The proposed method can be deployed in both semi-supervised and unsupervised training scenarios. The DBM-based posteriorgrams were evaluated on a series of keyword spotting tasks using the TIMIT speech corpus. In unsupervised training conditions, the DBM-approach improved upon our previous best unsupervised keyword detection performance using Gaussian mixture model-based posteriorgrams by over 10%. When limited amounts of labeled data were incorporated into training, the DBM-approach required less than one third of the annotated data in order to achieve a comparable performance of a system that used all of the annotated data for training.

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Section: Semi-supervised Dbm Posteriorgrammentioning

confidence: 99%

Resource configurable spoken query detection using Deep Boltzmann Machines

Zhang¹,

Salakhutdinov

Chang³

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In this case, the problem is known as Query-by-Example Spoken Term Detection (QbE-STD) and both the input query and the collection of documents are acoustic signals. Both of these tasks have been studied lately [1][2][3][4], and some examples of their interest and importance are the evaluation campaigns carried out in this line, such as the one organized by NIST in 2006 [5] and the MediaEval evaluations [6]. In this paper, we will focus on the Query-by-Example Spoken Term Detection task.…”

Section: Introductionmentioning

confidence: 99%

“…The feature vectors are usually a standard parametrization of the acoustic signal, for example based on cepstrals. Also, in the recent literature one of the most usual algorithms to perform this search is Segmental Dynamic Time Warping (SDTW) [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Phonetic-Based Approach to Query-by-Example Spoken Term Detection

Hurtado

Calvo

Gómez

et al. 2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. Query-by-Example Spoken Term Detection (QbE-STD) tasks are usually addressed by representing speech signals as a sequence of feature vectors by means of a parametrization step, and then using a pattern matching technique to find the candidate detections. In this paper, we propose a phoneme-based approach in which the acoustic frames are first converted into vectors representing the a posteriori probabilities for every phoneme. This strategy is specially useful when the language of the task is a priori known. Then, we show how this representation can be used for QbE-STD using both a Segmental Dynamic Time Warping algorithm and a graph-based method. The proposed approach has been evaluated with a QbE-STD task in Spanish, and the results show that it can be an adequate strategy for tackling this kind of problems.

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“…Our method only requires a forward pass computation of the neural network, followed by a vector distance computation, and therefore is more efficient than [15] where an LVCSR is involved and [17] where multiple DTW computations are necessary. It also requires less computation than [18,19] since vector distance is used instead of DTW.…”

Section: Introductionmentioning

confidence: 99%

“…In [17] graph-based method is proposed to embed audio segments into a fixed-dimensional space, but dynamic time warping (DTW) is performed between the test audio segment and all the training segments in order to compute the embedding, which can be slow given large number of training segments. In [18,19], Gaussian or phoneme posteriorgrams are generated as templates from example keywords, and DTW is used to compare the templates. Though this type of DTW-based methods have well-known inadequacies [17], it is the most appropriate KWS baseline for our application.…”

Section: Introductionmentioning

confidence: 99%

Query-by-example keyword spotting using long short-term memory networks

Chen

Parada

Sainath

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

135

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We present a novel approach to query-by-example keyword spotting (KWS) using a long short-term memory (LSTM) recurrent neural network-based feature extractor. In our approach, we represent each keyword using a fixed-length feature vector obtained by running the keyword audio through a word-based LSTM acoustic model. We use the activations prior to the softmax layer of the LSTM as our keyword-vector. At runtime, we detect the keyword by extracting the same feature vector from a sliding window and computing a simple similarity score between this test vector and the keyword vector. With clean speech, we achieve 86% relative false rejection rate reduction at 0.5% false alarm rate when compared to a competitive phoneme posteriorgram with dynamic time warping KWS system, while the reduction in the presence of babble noise is 67%. Our system has a small memory footprint, low computational cost, and high precision, making it suitable for on-device applications.

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Query-by-example spoken term detection using phonetic posteriorgram templates

Cited by 242 publications

References 9 publications

Resource configurable spoken query detection using Deep Boltzmann Machines

Resource configurable spoken query detection using Deep Boltzmann Machines

A Phonetic-Based Approach to Query-by-Example Spoken Term Detection

Query-by-example keyword spotting using long short-term memory networks

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