The Sheffield Wargames Corpus.

Recognition of speech in natural environments is a challenging task, even more so if this involves conversations between sev-eral speakers. Work on meeting recognition has addressed some of the significant challenges, mostly targeting formal, business style meetings where people are mostly in a static position in a room. Only limited data is available that contains high qual-ity near and far field data from real interactions between par-ticipants. In this paper we present a new corpus for research on speech recognition, speaker tracking and diarisation, based on recordings of native speakers of English playing a table-top wargame. The Sheffield Wargames Corpus comprises 7 hours of data from 10 recording sessions, obtained from 96 micro-phones, 3 video cameras and, most importantly, 3D location data provided by a sensor tracking system. The corpus repre-sents a unique resource, that provides for the first time location tracks (1.3Hz) of speakers that are constantly moving and talk-ing. The corpus is available for research purposes, and includes annotated development and evaluation test sets. Baseline results for close-talking and far field sets are included in this paper. 1

The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios

The CHiME challenges have played a significant role in the development and evaluation of robust automatic speech recognition (ASR) systems. We introduce the CHiME-7 distant ASR (DASR) task, within the 7th CHiME challenge. This task comprises joint ASR and diarization in far-field settings with multiple, and possibly heterogeneous, recording devices. Different from previous challenges, we evaluate systems on 3 diverse scenarios: CHiME-6, DiPCo, and Mixer 6. The goal is for participants to devise a single system that can generalize across different array geometries and use cases with no a-priori information. Another departure from earlier CHiME iterations is that participants are allowed to use open-source pre-trained models and datasets. In this paper, we describe the challenge design, motivation, and fundamental research questions in detail. We also present the baseline system, which is fully array-topology agnostic and features multi-channel diarization, channel selection, guided source separation and a robust ASR model that leverages self-supervised speech representations (SSLR)

CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings

Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the 6th CHiME Speech Separation and Recognition Challenge (CHiME-6). The new challenge revisits the previous CHiME-5 challenge and further considers the problem of distant multi-microphone conversational speech diarization and recognition in everyday home environments. Speech material is the same as the previous CHiME-5 recordings except for accurate array synchronization. The material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech. This paper provides a baseline description of the CHiME-6 challenge for both segmented multispeaker speech recognition (Track 1) and unsegmented multispeaker speech recognition (Track 2). Of note, Track 2 is the first challenge activity in the community to tackle an unsegmented multispeaker speech recognition scenario with a complete set of reproducible open source baselines providing speech enhancement, speaker diarization, and speech recognition modules

The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines

International audienceThe CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing , and machine learning. This paper introduces the 5th CHiME Challenge, which considers the task of distant multi-microphone conversational ASR in real home environments. Speech material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech and recorded by 6 Kinect microphone arrays and 4 binaural microphone pairs. The challenge features a single-array track and a multiple-array track and, for each track, distinct rankings will be produced for systems focusing on robustness with respect to distant-microphone capture vs. systems attempting to address all aspects of the task including conversational language modeling. We discuss the rationale for the challenge and provide a detailed description of the data collection procedure, the task, and the baseline systems for array synchronization, speech enhancement, and conventional and end-to-end ASR

A categorization of robust speech processing datasets

Speech and audio signal processing research is a tale of data collection efforts and evaluation campaigns. While large datasets for automatic speech recognition (ASR) in clean environments with various speaking styles are available, the landscape is not as picture- perfect when it comes to robust ASR in realistic environments, much less so for evaluation of source separation and speech enhancement methods. Many data collection efforts have been conducted, moving along towards more and more realistic conditions, each mak- ing different compromises between mostly antagonistic factors: financial and human cost; amount of collected data; availability and quality of annotations and ground truth; natural- ness of mixing conditions; naturalness of speech content and speaking style; naturalness of the background noise; etc. In order to better understand what directions need to be explored to build datasets that best support the development and evaluation of algorithms for recognition, separation or localization that can be used in real-world applications, we present here a study of existing datasets in terms of their key attributes

A study of speech distortion conditions in real scenarios for speech processing applications

International audienceThe growing demand for robust speech processing applications able to operate in adverse scenarios calls for new evaluation protocols and datasets beyond artificial laboratory conditions. The characteristics of real data for a given scenario are rarely discussed in the literature. As a result, methods are often tested based on the author expertise and not always in scenarios with actual practical value. This paper aims to open this discussion by identifying some of the main problems with data simulation or collection procedures used so far and summarizing the important characteristics of real scenarios to be taken into account, including the properties of reverberation, noise and Lombard effect. At last, we provide some preliminary guidelines towards designing experimental setup and speech recognition results for proposal validation

Speech processing using digital MEMS microphones

The last few years have seen the start of a unique change in microphones for consumer devices such as smartphones or tablets. Almost all analogue capacitive microphones are being replaced by digital silicon microphones or MEMS microphones. MEMS microphones perform differently to conventional analogue microphones. Their greatest disadvantage is significantly increased self-noise or decreased SNR, while their most significant benefits are ease of design and manufacturing and improved sensitivity matching. This thesis presents research on speech processing, comparing conventional analogue microphones with the newly available digital MEMS microphones. Specifically, voice activity detection, speaker diarisation (who spoke when), speech separation and speech recognition are looked at in detail. In order to carry out this research different microphone arrays were built using digital MEMS microphones and corpora were recorded to test existing algorithms and devise new ones. Some corpora that were created for the purpose of this research will be released to the public in 2013. It was found that the most commonly used VAD algorithm in current state-of-theart diarisation systems is not the best-performing one, i.e. MLP-based voice activity detection consistently outperforms the more frequently used GMM-HMM-based VAD schemes. In addition, an algorithm was derived that can determine the number of active speakers in a meeting recording given audio data from a microphone array of known geometry, leading to improved diarisation results. Finally, speech separation experiments were carried out using different post-filtering algorithms, matching or exceeding current state-of-the art results. The performance of the algorithms and methods presented in this thesis was verified by comparing their output using speech recognition tools and simple MLLR adaptation and the results are presented as word error rates, an easily comprehensible scale. To summarise, using speech recognition and speech separation experiments, this thesis demonstrates that the significantly reduced SNR of the MEMS microphone can be compensated for with well established adaptation techniques such as MLLR. MEMS microphones do not affect voice activity detection and speaker diarisation performance

Simulating realistic multiparty speech data: for the development of distant microphone ASR systems

Automatic speech recognition has become a ubiquitous technology integrated into our daily lives. However, the problem remains challenging when the speaker is far away from the microphone. In such scenarios, the speech is degraded both by reverberation and by the presence of additive noise. This situation is particularly challenging when there are competing speakers present (i.e. multi-party scenarios) Acoustic scene simulation has been a major tool for training and developing distant microphone speech recognition systems, and is now being used to develop solutions for mult-party scenarios. It has been used both in training -- as it allows cheap generation of limitless amounts of data -- and for evaluation -- because it can provide easy access to a ground truth (i.e. a noise-free target signal). However, whilst much work has been conducted to produce realistic artificial scene simulators, the signals produced from such simulators are only as good as the `metadata' being used to define the setups, i.e., the data describing, for example, the number of speakers and their distribution relative to the microphones. This thesis looks at how realistic metadata can be derived by analysing how speakers behave in real domestic environments. In particular, how to produce scenes that provide a realistic distribution for various factors that are known to influence the 'difficulty' of the scene, including the separation angle between speakers, the absolute and relative distances of speakers to microphones, and the pattern of temporal overlap of speech. Using an existing audio-visual multi-party conversational dataset, CHiME-5, each of these aspects has been studied in turn. First, producing a realistic angular separation between speakers allows for algorithms which enhance signals based on the direction of arrival to be fairly evaluated, reducing the mismatch between real and simulated data. This was estimated using automatic people detection techniques in video recordings from CHiME-5. Results show that commonly used datasets of simulated signals do not follow a realistic distribution, and when a realistic distribution is enforced, a significant drop in performance is observed. Second, by using multiple cameras it has been possible to estimate the 2-D positions of people inside each scene. This has allowed the estimation of realistic distributions for the absolute distance to the microphone and relative distance to the competing speaker. The results show grouping behaviour among participants when located in a room and the impact this has on performance depends on the room size considered. Finally, the amount of overlap and points in the mixture which contain overlap were explored using finite-state models. These models allowed for mixtures to be generated, which approached the overlap patterns observed in the real data. Features derived from these models were also shown to be a predictor of the difficulty of the mixture. At each stage of the project, simulated datasets derived using the realistic metadata distributions have been compared to existing standard datasets that use naive or uninformed metadata distributions, and implications for speech recognition performance are observed and discussed. This work has demonstrated how unrealistic approaches can produce over-promising results, and can bias research towards techniques that might not work well in practice. Results will also be valuable in informing the design of future simulated datasets