Post-processing of the recognized speech for web presentation of large audio archive

Bohac, Marek; Blavka, Karel; Kucharova, Michaela; Škodová, Svatava

doi:10.1109/tsp.2012.6256332

Cited by 9 publications

(3 citation statements)

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“…Bohac et al [1] compared the human ability to punctuate recognized spontaneous speech. They asked 10 people (correctors) to punctuate about 30 minutes of ASR transcripts in Czech.…”

Section: Sentence Boundary Evaluationmentioning

confidence: 99%

“…To our knowledge, the amount of studies that have tried to target the sentence boundary evaluation with a multi-reference approach is very small. In [1], Bohac et al evaluated the overall punctuation accuracy for Czech in a straightforward multi-reference framework. They considered a period (.)…”

Section: Sentence Boundary Evaluationmentioning

confidence: 99%

“…They rely on the transcript content to extract features like bag-of-word, POS tags or word embeddings [7,12,16,18,24,27,31]. Mixture of acoustic and lexical features have also been explored [1,13,14,33], which is advantageous when both audio signal and transcript are available.…”

Section: Sentence Boundary Evaluationmentioning

confidence: 99%

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WiSeBE: Window-Based Sentence Boundary Evaluation

González-Gallardo

Torres‐Moreno

2018

Advances in Computational Intelligence

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Sentence Boundary Detection (SBD) has been a major research topic since Automatic Speech Recognition transcripts have been used for further Natural Language Processing tasks like Part of Speech Tagging, Question Answering or Automatic Summarization. But what about evaluation? Do standard evaluation metrics like precision, recall, F-score or classification error; and more important, evaluating an automatic system against a unique reference is enough to conclude how well a SBD system is performing given the final application of the transcript? In this paper we propose Window-based Sentence Boundary Evaluation (WiSeBE), a semi-supervised metric for evaluating Sentence Boundary Detection systems based on multi-reference (dis)agreement. We evaluate and compare the performance of different SBD systems over a set of Youtube transcripts using WiSeBE and standard metrics. This double evaluation gives an understanding of how WiSeBE is a more reliable metric for the SBD task.

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“…Bohac et al [1] compared the human ability to punctuate recognized spontaneous speech. They asked 10 people (correctors) to punctuate about 30 minutes of ASR transcripts in Czech.…”

Section: Sentence Boundary Evaluationmentioning

confidence: 99%

Section: Sentence Boundary Evaluationmentioning

confidence: 99%