BLSTM harvesting of auxiliary NCHLT speech data

Badenhorst, Jaco; Martinus, Laura; Wet, Febe de

doi:10.1109/robomech.2019.8704835

Cited by 1 publication

(9 citation statements)

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“…The development of TDNN-BLSTM baseline acoustic models for all 11 languages was described in [19]. In this paper, the aim was to improve on the baseline by using TDNN-F acoustic models (Section 2).…”

Section: Acoustic Modelingmentioning

confidence: 99%

“…In this paper, the aim was to improve on the baseline by using TDNN-F acoustic models (Section 2). To create the new models, the same standard triphone recognition systems that were used in previous studies [19] were required to extract phone alignments for the training data.…”

Section: Acoustic Modelingmentioning

confidence: 99%

“…The selected language-scale parameters were subsequently used during data harvesting to gauge recognition performance. Table 7 compares the development (dev) and test (tst) set PER results of the TDNN-BLSTM baseline [19] with the new TDNN-F acoustic models. Both the TDNN-F 1c and TDNN-F 1d recipes (see Section 4.1) were evaluated for all 11 languages.…”

Section: Phone Recognition Measurementmentioning

confidence: 99%

“…A phone string corresponding to the expected pronunciation of each prompt was generated, and if a forced alignment between the phone string and the actual acoustics failed, the utterance was not included in the auxiliary data. For the remaining prompts, we used a phone-based dynamic programming (PDP) scoring technique [18,19] to quantify the degree of acoustic match between the expected and produced pronunciations of each prompt and to rank them accordingly. Consequently, transcription errors or bad acoustic recording conditions could be filtered out based on an utterance level measure.…”

Section: Introductionmentioning

confidence: 99%

“…A model architecture that combines TDNNs and bi-directional LSTMs (BLSTMs) yielded the best results in a preliminary study on the auxiliary NCHLT data [19]. BLSTMs process input data in both time directions using two separate hidden layers.…”

Section: Introductionmentioning

confidence: 99%

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The Usefulness of Imperfect Speech Data for ASR Development in Low-Resource Languages

Badenhorst¹,

Wet

2019

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When the National Centre for Human Language Technology (NCHLT) Speech corpus was released, it created various opportunities for speech technology development in the 11 official, but critically under-resourced, languages of South Africa. Since then, the substantial improvements in acoustic modeling that deep architectures achieved for well-resourced languages ushered in a new data requirement: their development requires hundreds of hours of speech. A suitable strategy for the enlargement of speech resources for the South African languages is therefore required. The first possibility was to look for data that has already been collected but has not been included in an existing corpus. Additional data was collected during the NCHLT project that was not included in the official corpus: it only contains a curated, but limited subset of the data. In this paper, we first analyze the additional resources that could be harvested from the auxiliary NCHLT data. We also measure the effect of this data on acoustic modeling. The analysis incorporates recent factorized time-delay neural networks (TDNN-F). These models significantly reduce phone error rates for all languages. In addition, data augmentation and cross-corpus validation experiments for a number of the datasets illustrate the utility of the auxiliary NCHLT data.

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Section: Acoustic Modelingmentioning

confidence: 99%

Section: Acoustic Modelingmentioning

confidence: 99%

Section: Phone Recognition Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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