In this paper, we present a systems approach for channel mod-eling of an Automatic Speech Recognition (ASR) system. This can have implications in improving speech recognition com-ponents, such as through discriminative language modeling. We simulate the ASR corruption using a phrase-based machine translation system trained between the reference phoneme and output phoneme sequences of a real ASR. We demonstrate that local optimization on the quality of phoneme-to-phoneme map-pings does not directly translate to overall improvement of the entire model. However, we are still able to capitalize on contex-tual information of the phonemes which a simple acoustic dis-tance model is not able to accomplish. Hence we show that the use of longer context results in a significantly improved model of the ASR channel.

Engineering automatic speech recognition (ASR) for speech to speech (S2S) translation systems, especially targeting languages and domains that do not have readily available spoken language resources, is immensely challenging due to a number of reasons. In addition to contending with the conventional data-hungry speech acoustic and language modeling needs, these designs have to accommodate varying requirements imposed by the domain needs and characteristics, target device and usage modality (such as phrase-based, or spontaneous free form interactions, with or without visual feedback) and huge spoken language variability arising due to socio-linguistic and cultural differences of the users. This paper, using case studies of creating speech translation systems between English and languages such as Pashto and Farsi, describes some of the practical issues and the solutions that were developed for multilingual ASR development. These include novel acoustic and language modeling strategies such as language adaptive recognition, active-learning based language modeling, class-based language models that can better exploit resource poor language data, efficient search strategies, including N-best and confidence generation to aid multiple hypotheses translation, use of dialog information and clever interface choices to facilitate ASR, and audio interface design for meeting both usability and robustness requirements