Implementation of Vocal Tract Length Normalization for Phoneme Recognition on TIMIT Speech Corpus

Abstract. Inter-speaker variability, one of the problems faced in speech recognition system, has caused the performance degradation in recognizing varied speech spoken by different speakers. Vocal Tract Length Normalization (VTLN) method is known to improve the recognition performances by compensating the speech signal using specific warping factor. Experiments are conducted using TIMIT speech corpus and Hidden Markov Model Toolkit (HTK) together with the implementation of VTLN method in order to show improvement in speaker independent phoneme recognition. The results show better recognition performance using Bigram Language Model compared to Unigram Language Model, with Phoneme Error Rate (PER) 28.8% as the best recognition performance for Bigram and PER 38.09% for Unigram. The best warp factor used for normalization in this experiment is 1.40

Evaluation of automatic transcription systems for the judicial domain

This paper describes two different automatic transcription systems developed for judicial application domains for the Polish and Italian languages. The judicial domain requires to cope with several factors which are known to be critical for automatic speech recognition, such as: background noise, reverberation, spontaneous and accented speech, overlapped speech, cross channel effects, etc. The two automatic speech recognition (ASR) systems have been developed independently starting from out-of-domain data and, then, they have been adapted to the judicial domain using a certain amount of in-domain audio and text data. The ASR performance have been measured on audio data acquired in the courtrooms of Naples and Wroclaw. The resulting word error rates are around 40%, for Italian, and around between 30% and 50% for Polish. This performance, similar to that reported for other comparable ASR tasks (e.g. meeting transcriptions with distant microphone), suggests that possible applications can address tasks such as indexing and/or information retrieval in multimedia documents recorded during judicial debates

Acoustic variability and automatic recognition of children’s speech

International audienc

Towards age-independent acoustic modeling

International audienceIn automatic speech recognition applications, due to significant differences in voice characteristics, adults and children are usually treated as two population groups, for which different acoustic models are trained. In this paper, age-independent acoustic modeling is investigated in the context of large vocabulary speech recognition. Exploiting a small amount (9 hours) of children's speech and a more significant amount (57 hours) of adult speech, age-independent acoustic models are trained using several methods for speaker adaptive acoustic modeling. Recognition results achieved using these models are compared with those achieved using age-dependent acoustic models for children and adults, respectively. Recognition experiments are performed on four Italian speech corpora, two consisting of children's speech and two of adult speech, using 64k word and 11k word trigram language models. Methods for speaker adaptive acoustic modeling prove to be effective for training age-independent acoustic models ensuring recognition results at least as good as those achieved with age-dependent acoustic models for adults and children

A Situational Analysis of Current Speech-Synthesis Systems for Child Voices: A Scoping Review of Qualitative and Quantitative Evidence

Background: Speech synthesis has customarily focused on adult speech, but with the rapid development of speech-synthesis technology, it is now possible to create child voices with a limited amount of child-speech data. This scoping review summarises the evidence base related to developing synthesised speech for children. (2) Method: The included studies were those that were (1) published between 2006 and 2021 and (2) included child participants or voices of children aged between 2–16 years old. (3) Results: 58 studies were identified. They were discussed based on the languages used, the speech-synthesis systems and/or methods used, the speech data used, the intelligibility of the speech and the ages of the voices. Based on the reviewed studies, relative to adult-speech synthesis, developing child-speech synthesis is notably more challenging. Child speech often presents with acoustic variability and articulatory errors. To account for this, researchers have most often attempted to adapt adult-speech models, using a variety of different adaptation techniques. (4) Conclusions: Adapting adult speech has proven successful in child-speech synthesis. It appears that the resulting quality can be improved by training a large amount of pre-selected speech data, aided by a neural-network classifier, to better match the children’s speech. We encourage future research surrounding individualised synthetic speech for children with CCN, with special attention to children who make use of low-resource languages

Improved automatic speech recognition through speaker normalization

In this paper, speaker adaptive acoustic modeling is investigated by using a novel method for speaker normalization and a well known vocal tract length normalization method. With the novel normalization method, acoustic observations of training and testing speakers are mapped into a normalized acoustic space through speaker-specific transformations with the aim of reducing inter-speaker acoustic variability. For each speaker, an affine transformation is estimated with the goal of reducing the mismatch between the acoustic data of the speaker and a set of target hidden Markov models. This transformation is estimated through constrained maximum likelihood linear regression and then applied to map the acoustic observations of the speaker into the normalized acoustic space. Recognition experiments made use of two corpora, the first one consisting of adults` speech, the second one consisting of children`s speech. Performing training and recognition with normalized data resulted in a consistent reduction of the word error rate with respect to the baseline systems trained on unnormalized data. In addition, the novel method always performed better than the reference vocal tract length normalization method adopted in this work. When unsupervised static speaker adaptation was applied in combination with each of the two speaker normalization methods, a different behavior was observed on the two corpora: in one case performance became very similar while in the other case the difference remained significant

Improved Automatic Speech Recognition through Speaker Normalization

In this paper, speaker adaptive acoutstic modeling is investigated by using a novel method for speaker normalization and a wel known vocal tract lenght normalization method. With the novel normalization method, acoustic observations of training and testing speakers are mapped into a normalized acoustic space through speaker-specific transofrmations with the aim of reducing inter-speaker acoustic variability. For each speaker, an affine transformation is estimated with the goal of reducing the mismatch between the acoustic data of the speaker and a set of target hidden Markov models. This transformation is estimated through contrained maximum likelihood linear regression and then applied to map the acoustic observations of the speaker into the mormalized acoustic space. Recognition experiments made use of two corpora, the first one consisting of adults'speech, the second one consisting of children's speech. Performing training and recognition with normalized data resulted ina consistent reduction of the word error rate with respect to the baseline systems trained on unnormalized data. In addition, the novel method always performed better than the reference vocal tract lenght normalization method adopted in this work. However, it was found that when unsupervised static speaker adaptation is applied in combination with speaker normalization, recognition performance tends to be similar independently of the speaker normalization method adopte