Distant Speech Recognition

The REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge is a benchmark for evaluation of automatic speech recognition techniques. The challenge assumes the scenario of capturing utterances spoken by a single stationary distant-talking speaker with 1-channe, 2-channel or 8-channel microphone-arrays in reverberant meeting rooms. It features both real recordings and simulated data. The challenge constis of speech enhancement and automatic speech recognition tasks in reverberant environments. The speech enhancement challenge task consists of enhancing noisy reverberant speech with single-/multi-channel speech enhancement techniques, and evaluating the enhanced data in terms of objective and subjective evaluation metrics. The automatic speech recognition challenge task consists of improving the recognition accuracy of the same reverberant speech. The background noise is mostly stationary and the signal-to-noise ratio is modest. Source: https://reverb2014.audiolabs-erlangen.de

DIRHA-English is a multi-microphone database composed of real and simulated sequences of 1-minute. The overall corpus is composed of different types of sequences including: 1) Phonetically-rich sentences; 2) WSJ 5-k utterances; 3) WSJ 20-k utterances; 4) Conversational speech (also including keywords and commands). The sequences are available for both UK and US English at 48 kHz. The DIRHA-English dataset offers the possibility to work with a very large number of microphone channels, to use of microphone arrays having different characteristics and to work considering different speech recognition tasks (e.g., phone-loop, keyword spotting, ASR with small and very large language models). Source: The DIRHA-English Corpus Image Source: https://arxiv.org/pdf/1710.02560v1.pdf

We present a speech data corpus that simulates a "dinner party" scenario taking place in an everyday home environment. The corpus was created by recording multiple groups of four Amazon employee volunteers having a natural conversation in English around a dining table. The participants were recorded by a single-channel close-talk microphone and by five far-field 7-microphone array devices positioned at different locations in the recording room. The dataset contains the audio recordings and human labeled transcripts of a total of 10 sessions with a duration between 15 and 45 minutes. The corpus was created to advance in the field of noise robust and distant speech processing and is intended to serve as a public research and benchmarking data set.

BERSt Dataset We release the BERSt Dataset for various speech recognition tasks including Automatic Speech Recognition (ASR) and Speech Emotion Recogniton (SER) Overview 4526 single phrase recordings (~3.75h) 98 professional actors 19 phone positions 7 emotion classes 3 vocal intensity levels varied regional and non-native English accents nonsense phrases covering all English Phonemes Data collection The BERSt dataset represents data collected in home envrionments using various smartphone microphones (phone model available as metadata) Participants were around the globe and represent varying regional accents in English: UK, Canada, USA (multi-state), Australia, including a subset of the data that is non-native English speakers including: French, Russian, Hindi etc. The data includes 13 non-sense phrases for use cases robust to lingustic context and high surprisal. Partipants were prompted to speak, raise their voice and shout each phrase while moving their phone to various distances and locations in their home, as well as with various obstructions to the microphone, e.g. in a backpack Baseline results of various state-of-the-art methods for ASR and SER show that this dataset remains a challenging task, and we encourage researchers to use this data to fine-tune and benchmark their models in these difficult condition representing possible real world situations Affect annotations are those provided to the actors, they have not been validated through perception The speech annotations, however, has been checked and adjusted to mistakes in the speech. Data splits and organisation For each phone position and phrase, the actors provided a single recording for the three vocal intensity levels, these raw audio files are available Meta-data in csv format corresponds to the files split per utterance with noise and silence before and after speech removed, found inside clean_clips for each data splits We provide a test, train and validation split There is no speaker cross-over between splits, the train and validation sets each contain 10 speakers not seen in the training set Metadata Details actor count 98 Gender counts Woman: 61 Man: 34 Non-Binary: 1 Prefer not to disclose 2 Current daily language counts English: 95 Norwegian: 1 Russian: 1 French: 1 First language counts English: 75 Non English: 23 Spanish: 6 French: 3 Portuguese: 3 Chinese: 2 Norwegian: 1 Mandarin: 1 Tagalog: 1 Italian: 1 Hungarian: 1 Russian: 1 Hindi: 1 Swahili: 1 Croatian: 1 Pre-split Data counts Emotion counts fear: 236 neutral: 234 disgust: 232 joy: 224 anger: 223 surprise: 210 sadness: 201 Distance counts: Near body: 627 1-2m away: 324 Other side of room: 316 Outside of room: 293