Exploiting Chinese character models to improve speech recognition performance

The Chinese language is based on characters which are syllabic in nature. Since languages have syllabotactic rules which govern the construction of syllables and their allowed sequences, Chinese character sequence models can be used as a first level approximation of allowed syllable sequences. N-gram character sequence models were trained on 4.3 billion characters. Characters are used as a first level recognition unit with multiple pronunciations per character. For comparison the CU-HTK Mandarin word based system was used to recognize words which were then converted to character sequences. The character only system error rates for one best recognition were slightly worse than word based character recognition. However combining the two systems using log-linear combination gives better results than either system separately. An equally weighted combination gave consistent CER gains of 0.1-0.2% absolute over the word based standard system. Copyright © 2009 ISCA.

Patterns of interaction in construction team meetingsξ

Sir John Egan’s 1998 report on the construction industry (Construction Task Force 1998) noted its confrontational and adversarial nature. Both the original report and its subsequent endorsement in Accelerating Change (Strategic Forum 2002) called for improved working relationships—so-called ‘integration’—within and between both design and construction aspects. In this paper, we report on our observations of on-site team meetings for a major UK project during its construction phase. We attended a series of team meetings and recorded the patterns of verbal interaction that took place within them. In reporting our findings, we have deliberately used a graphical method for presenting the results, in the expectation that this will make them more readily accessible to designers. Our diagrams of these interaction patterns have already proved to be intuitively and quickly understood, and have generated interest and discussion among both those we observed and others who have seen them. We noted that different patterns of communication occurred in different types of meetings. Specifically, in the problem-solving meeting, there was a richness of interaction that was largely missing from progress meetings and technical meetings. Team members expressed greater satisfaction with this problem-solving meeting where these enriched exchanges took place. By making comparisons between the different patterns, we are also able to explore functional roles and their interactions. From this and other published evidence, we conclude that good teamworking practices depend on a complex interplay of relations and dependencies embedded within the team.

Syllable language models for Mandarin speech recognition: exploiting character language models.

Mandarin Chinese is based on characters which are syllabic in nature and morphological in meaning. All spoken languages have syllabiotactic rules which govern the construction of syllables and their allowed sequences. These constraints are not as restrictive as those learned from word sequences, but they can provide additional useful linguistic information. Hence, it is possible to improve speech recognition performance by appropriately combining these two types of constraints. For the Chinese language considered in this paper, character level language models (LMs) can be used as a first level approximation to allowed syllable sequences. To test this idea, word and character level n-gram LMs were trained on 2.8 billion words (equivalent to 4.3 billion characters) of texts from a wide collection of text sources. Both hypothesis and model based combination techniques were investigated to combine word and character level LMs. Significant character error rate reductions up to 7.3% relative were obtained on a state-of-the-art Mandarin Chinese broadcast audio recognition task using an adapted history dependent multi-level LM that performs a log-linearly combination of character and word level LMs. This supports the hypothesis that character or syllable sequence models are useful for improving Mandarin speech recognition performance.