PURPOSE Children acquire /-z/ syllabic plurals (e.g., bus es) later than /-s, -z/ segmental plurals (e.g., cat s, dog s). In this study, the authors explored whether increased syllable number or segmental factors best explains poorer performance with syllabic plurals. METHOD An elicited imitation experiment was conducted with 14 two-year-olds involving 8 familiar disyllabic target plural noun...

We describe an algorithm to learn word pronunciations from acoustic data. The algorithm jointly optimizes the pronunciation of a word using (a) the acoustic match of this pronunciation to the observed data, and (b) how “linguistically reasonable” the pronunciation is. Variations of word pronunciations in the recognition dictionary (which was created by linguists), are used to train a model of w...

To improve the recognition accuracy for spontaneous conversational speech, we collected a corpus to study how spontaneous conversational speech differs from read style speech. The corpus consists of two parts: 1) spontaneous conversational speech and 2) read speech with the same word transcriptions as the conversational speech. In word and phone recognition experiments, it was confirmed that, f...

In this paper, we report on a study with the aim of automatically detecting phoneme-level mispronunciations in 32 French speakers suffering from unilateral facial palsy at four different clinical severity grades. We sought to determine if the Goodness of Pronunciation (GOP) algorithm, which is commonly used in Computer-Assisted Language Learning systems to detect learners’ individual errors, co...

We describe an algorithm to learn word pronunciations from acoustic data. The algorithm jointly optimizes the pronunciation of a word using (a) the acoustic match of this pronunciation to the observed data, and (b) how “linguistically reasonable” the pronunciation is. Variations of word pronunciations in the recognition dictionary (which was created by linguists), are used to train a model of w...

This paper describes an approach to identifying the reasons that speech recognition errors occur. The algorithm presented requires an accurate word transcript of the utterances being analyzed. It places errors into one of the categories: 1) due to outof-vocabulary (OOV) word spoken, 2) search error, 3) homophone substitution, 4) language model overwhelming correct acoustics, 5) transcript/pronu...

Pronunciation variations are common sources of recognition errors in real-world applications, so that specific techniques must be developed to handle them. We are describing a method to incorporate pronunciation alternatives that have been tested with both continuous and isolated word speech recognisers for Spanish. We present an automatic grapheme-tophoneme system, modified to generate alterna...

Computer Assisted Language Learning (CALL) has now established itself as a prolific area whose advantages are well-known to educators. Yet, many authors lament the lack of a reliable integrated conceptual framework linking technology advances and second language acquisition research within which effective materials can be designed [1],[2]. The CALL world has recently witnessed a flourishing of ...

In this paper, we propose a system that automatically generates templates for detecting Chinese character errors. We first collect the confusion sets for each high-frequency Chinese character. Error types include pronunciation-related errors and radical-related errors. With the help of the confusion sets, our system generates possible error patterns in context, which will be used as detection t...

For the purpose of developing pronunciation training tools for second language learning a corpus of non-native speech data has been collected, which consists of almost 18 hours of annotated speech signals spoken by Italian and German learners of English. The corpus is based on 250 utterances selected from typical second language learning exercises. It has been annotated at the word and the phon...