ASR error management for improving spoken language understanding

This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems. In this study, the SLU task consists in automatically extracting, from ASR transcriptions , semantic concepts and concept/values pairs in a e.g touristic information system. An approach is proposed for enriching the set of semantic labels with error specific labels and by using a recently proposed neural approach based on word embeddings to compute well calibrated ASR confidence measures. Experimental results are reported showing that it is possible to decrease significantly the Concept/Value Error Rate with a state of the art system, outperforming previously published results performance on the same experimental data. It also shown that combining an SLU approach based on conditional random fields with a neural encoder/decoder attention based architecture , it is possible to effectively identifying confidence islands and uncertain semantic output segments useful for deciding appropriate error handling actions by the dialogue manager strategy .Comment: Interspeech 2017, Aug 2017, Stockholm, Sweden. 201

The MGB Challenge: Evaluating Multi-genre Broadcast Media Recognition

This paper describes the Multi-Genre Broadcast (MGB) Challenge at ASRU 2015, an evaluation focused on speech recognition, speaker diarization, and "lightly supervised" alignment of BBC TV recordings. The challenge training data covered the whole range of seven weeks BBC TV output across four channels, resulting in about 1,600 hours of broadcast audio. In addition several hundred million words of BBC subtitle text was provided for language modelling. A novel aspect of the evaluation was the exploration of speech recognition and speaker diarization in a longitudinal setting - i.e. recognition of several episodes of the same show, and speaker diarization across these episodes, linking speakers. The longitudinal tasks also offered the opportunity for systems to make use of supplied metadata including show title, genre tag, and date/time of transmission. This paper describes the task data and evaluation process used in the MGB challenge, and summarises the results obtained

Speaker Diarization

Disertační práce se zaměřuje na téma diarizace řečníků, což je úloha zpracování řeči typicky charakterizovaná otázkou "Kdo kdy mluví?". Práce se také zabývá související úlohou detekce překrývající se řeči, která je velmi relevantní pro diarizaci. Teoretická část práce poskytuje přehled existujících metod diarizace řečníků, a to jak těch offline, tak online, a přibližuje několik problematických oblastí, které byly identifikovány v rané fázi autorčina výzkumu. V práci je také předloženo rozsáhlé srovnání existujících systémů se zaměřením na jejich uváděné výsledky. Jedna kapitola se také zaměřuje na téma překrývající se řeči a na metody její detekce. Experimentální část práce předkládá praktické výstupy, kterých bylo dosaženo. Experimenty s diarizací se zaměřovaly zejména na online systém založený na GMM a na i-vektorový systém, který měl offline i online varianty. Závěrečná sekce experimentů také přibližuje nově navrženou metodu pro detekci překrývající se řeči, která je založena na konvoluční neuronové síti.ObhájenoThe thesis focuses on the topic of speaker diarization, a speech processing task that is commonly characterized as the question "Who speaks when?". It also addresses the related task of overlapping speech detection, which is very relevant for diarization. The theoretical part of the thesis provides an overview of existing diarization approaches, both offline and online, and discusses some of the problematic areas which were identified in early stages of the author's research. The thesis also includes an extensive comparison of existing diarization systems, with focus on their reported performance. One chapter is also dedicated to the topic of overlapping speech and the methods of its detection. The experimental part of the thesis then presents the work which has been done on speaker diarization, which was focused mostly on a GMM-based online diarization system and an i-vector based system with both offline and online variants. The final section also details a newly proposed approach for detecting overlapping speech using a convolutional neural network