Data-driven generation of phonetic broad classes, based on phoneme confusion matrix similarity

Žgank, Andrej; Horvat, Bogomir; Kačič, Zdravko

doi:10.1016/j.specom.2005.03.011

Cited by 17 publications

(12 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several similarity measure proposals can be found in literature [4,16,25], but as referred in [26], they do not fulfil the properties of a proper metric. In the present proposal, d 1 has the three metric properties: it is positive, symmetric and satisfies the triangle inequality.…”

Section: Similarity Measurementioning

confidence: 99%

“…Broad phonetic classes (BPC) have widely been used in speech recognition research as, for instance, automatic language identification [1]; speaking rate estimation [2]; multi lingual systems [3,4] and, especially, in phone recognition [5][6][7][8]. Its success is due to the carried additional information that contributes to improve the recognition rates, especially under noise conditions [9].…”

Section: Introductionmentioning

confidence: 99%

“…The key point of all clustering algorithms is the choice of a proximity or distance measure. This measure can be obtained from acoustic models, e.g., [3,15], or even rely on the confusion matrix, e.g., [4,16]. Model-driven methods and confusiondriven a methods are then the two major categories of datadriven phone clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…This approach was already proposed in [4], where the phones are grouped using rules depending on a set of weights and thresholds. Our proposal differs from [4] in so far as we define a metric to compute phone distances.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Broad phonetic class definition driven by phone confusions

Lopes

Perdigão

2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Intermediate representations between the speech signal and phones may be used to improve discrimination among phones that are often confused. These representations are usually found according to broad phonetic classes, which are defined by a phonetician. This article proposes an alternative data-driven method to generate these classes. Phone confusion information from the analysis of the output of a phone recognition system is used to find clusters at high risk of mutual confusion. A metric is defined to compute the distance between phones. The results, using TIMIT data, show that the proposed confusion-driven phone clustering method is an attractive alternative to the approaches based on human knowledge. A hierarchical classification structure to improve phone recognition is also proposed using a discriminative weight training method. Experiments show improvements in phone recognition on the TIMIT database compared to a baseline system.

show abstract

Section: Similarity Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Broad phonetic class definition driven by phone confusions

Lopes

Perdigão

2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…In the works done in [3] and [4], PCMs were used to enhance ASR performance. In [3], a comprehensive comparison was made between an expert-generated and a data-driven PCM.…”

Section: Introductionmentioning

confidence: 99%

EM-based phoneme confusion matrix generation for low-resource spoken term detection

Wang

Metze

2014

2014 IEEE Spoken Language Technology Workshop (SLT)

View full text Add to dashboard Cite

The idea of using a data-driven phoneme confusion matrix (PCM) to enhance speech recognition and retrieval performance is not new to the speech community. Although empirical results show various degrees of improvements brought by introducing a PCM, the underlying data-driven processes introduced in most papers are rather ad-hoc and lack rigorous statistical justifications. In this paper we will focus on the statistical aspects of PCM generation, propose and justify a novel expectationmaximization based algorithm for data-driven PCM generation. We will evaluate the performance of the generated PCMs under the context of low-resource spoken term detection, with primary focus on out-of-vocabulary keywords.

show abstract