Alexandre Nanchen

MediaParl: Bilingual mixed language accented speech database

MediaParl is a Swiss accented bilingual database containing recordings in both French and German as they are spoken in Switzerland. The data were recorded at the Valais Parliament. Valais is a bilingual Swiss canton with many local accents and dialects. Therefore, the database contains data with high variability and is suitable to study multilingual, accented and non-native speech recognition as well as language identification and language switch detection. We also define monolingual and mixed language automatic speech recognition and language identification tasks and evaluate baseline systems. The database is publicly available for download.

Exploiting un-transcribed foreign data for speech recognition in well-resourced languages

Manual transcription of audio databases for automatic speech recognition (ASR) training is a costly and time-consuming process. State-of-the-art hybrid ASR systems that are based on deep neural networks (DNN) can exploit un-transcribed foreign data during unsupervised DNN pre-training or semi-supervised DNN training. We investigate the relevance of foreign data characteristics, in particular domain and language. Using three different datasets of the MediaParl and Ester databases, our experiments suggest that domain and language are equally important. Foreign data recorded under matched conditions (language and domain) yields the most improvement. The resulting ASR system yields about 5% relative improvement compared to the baseline system only trained on transcribed data. Our studies also reveal that the amount of foreign data used for semi-supervised training can be significantly reduced without degrading the ASR performance if confidence measure based data selection is employed.

Automatic content linking: speech-based just-in-time retrieval for multimedia archives

The Automatic Content Linking Device monitors a conversation and uses automatically recognized words to retrieve documents that are of potential use to the participants. The document set includes project related reports or emails, transcribed snippets of past meetings, and websites. Retrieval results are displayed at regular intervals.

The ACLD: speech-based just-in-time retrieval of meeting transcripts, documents and websites

The Automatic Content Linking Device (ACLD) is a just-in-time retrieval system that monitors an ongoing conversation or a monologue and enriches it with potentially related documents, including transcripts of past meetings, from local repositories or from the Internet. The linked content is displayed in real-time to the participants in the conversation, or to users watching a recorded conversation or talk. The system can be demonstrated in both settings, using real-time automatic speech recognition (ASR) or replaying offline ASR, via a flexible user interface that displays results and provides access to the content of past meetings and documents.