On-line garbage modeling for word and utterance verification in natural numbers recognition

Torre, Celinda de la; Gómez, Luis A. Hernández; Caminero-Gil, F. Javier; Alamo, Cesar Martín del

doi:10.1109/icassp.1996.543253

“…

Out-of-vocabulary (OOV) utterance detection and rejection are specially important and difficult problems in large-vocabulary and continuous speech recognition. In [1] we proposed an utterance verification procedure based on the use of frame-by-frame best acoustic state scores instead of using explicit garbage models. This procedure is usually referred to as on-line garbage modeling.In this contribution we extend our previous work in two major directions: a) we analyze, through the use of Discriminant Analysis, the possibilities of using L-best local scores and N-best utterance hypotheses scores for utterance verification; b) we present experimental results not only for a spontaneously spoken natural number recognition task, as in [1], but also for a flexible large vocabulary recognition task.

…”

mentioning

confidence: 99%

“…In [1] we proposed an utterance verification procedure based on the use of frame-by-frame best acoustic state scores instead of using explicit garbage models. This procedure is usually referred to as on-line garbage modeling.In this contribution we extend our previous work in two major directions: a) we analyze, through the use of Discriminant Analysis, the possibilities of using L-best local scores and N-best utterance hypotheses scores for utterance verification; b) we present experimental results not only for a spontaneously spoken natural number recognition task, as in [1], but also for a flexible large vocabulary recognition task. All the results, based on a telephone database, show that the proposed on-line garbage modeling procedure outperforms, both in performance and computational cost, to other approaches based on the use of explicit garbage models.…”

mentioning

confidence: 99%

“…Out-of-vocabulary (OOV) utterance detection and rejection are specially important and difficult problems in large-vocabulary and continuous speech recognition. In [1] we proposed an utterance verification procedure based on the use of frame-by-frame best acoustic state scores instead of using explicit garbage models. This procedure is usually referred to as on-line garbage modeling.…”

mentioning

confidence: 99%

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On-line garbage modeling with discriminant analysis for utterance verification

Caminero-Gil¹,

Torre²,

Villarrubia³

et al. 1996

4th International Conference on Spoken Language Processing (ICSLP 1996)

0

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Out-of-vocabulary (OOV) utterance detection and rejection are specially important and difficult problems in large-vocabulary and continuous speech recognition. In [1] we proposed an utterance verification procedure based on the use of frame-by-frame best acoustic state scores instead of using explicit garbage models. This procedure is usually referred to as on-line garbage modeling.In this contribution we extend our previous work in two major directions: a) we analyze, through the use of Discriminant Analysis, the possibilities of using L-best local scores and N-best utterance hypotheses scores for utterance verification; b) we present experimental results not only for a spontaneously spoken natural number recognition task, as in [1], but also for a flexible large vocabulary recognition task. All the results, based on a telephone database, show that the proposed on-line garbage modeling procedure outperforms, both in performance and computational cost, to other approaches based on the use of explicit garbage models.

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“…The selected front-end was the one used by Telefónica I+D for some of its telephone applications [1] [2] [3]. The Speech Signal digitalized at 8 kHz, is pre-emphasized by a factor of alpha=0.97.…”

Section: Baseline Systemmentioning

confidence: 99%

Evaluation of the Telefonica I+D Natural Numbers Recognizer over different dialects of Spanish from Spain and America

Torre

¹

,

Caminero-Gil²,

Álvarez³

et al.

Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96

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In this paper we present the results obtained when evaluating the Natural Numbers Recognizer of Telefónica I+D over some particular dialects of Spanish from Spain and America. The evaluation was made over two different data sets corresponding to two different situations. A first set includes dialects of Spanish from Spain, that were considered in the training and design of our baseline system, and a second set corresponds to Argentinian Spanish, that was not considered to train the original system. Just because we are interested in a system able to be used by a wide range of users, we tested the possibilities of MAP (Maximum-A-Priori techniques) to adapt the original HMMs in order to represent all the dialects. The experimental results show the capabilities of our recognizer to be used in applications spread over a great number of Spanishspeaking countries.

show abstract

“…In [1] we proposed an utterance verification procedure based on the use of frame-by-frame best acoustic state scores instead of using explicit garbage models. This procedure is usually referred to as on-line garbage modeling.…”

mentioning

confidence: 99%

On-line garbage modeling with discriminant analysis for utterance verification

Caminero

¹

,

Torre²,

Villarrubia³

et al.

Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96

View full text Add to dashboard Cite

Out-of-vocabulary (OOV) utterance detection and rejection are specially important and difficult problems in large-vocabulary and continuous speech recognition. In [1] we proposed an utterance verification procedure based on the use of frame-by-frame best acoustic state scores instead of using explicit garbage models. This procedure is usually referred to as on-line garbage modeling.In this contribution we extend our previous work in two major directions: a) we analyze, through the use of Discriminant Analysis, the possibilities of using L-best local scores and N-best utterance hypotheses scores for utterance verification; b) we present experimental results not only for a spontaneously spoken natural number recognition task, as in [1], but also for a flexible large vocabulary recognition task. All the results, based on a telephone database, show that the proposed on-line garbage modeling procedure outperforms, both in performance and computational cost, to other approaches based on the use of explicit garbage models.

show abstract

On-line garbage modeling for word and utterance verification in natural numbers recognition

Cited by 6 publications

References 5 publications

On-line garbage modeling with discriminant analysis for utterance verification

On-line garbage modeling with discriminant analysis for utterance verification

Evaluation of the Telefonica I+D Natural Numbers Recognizer over different dialects of Spanish from Spain and America

On-line garbage modeling with discriminant analysis for utterance verification

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