ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications

Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and controlling the airspace in a safe and optimal manner. These voice-based dialogues are carried between an air traffic controller (ATCO) and pilots via very-high frequency radio channels. In order to incorporate these novel technologies into ATC (low-resource domain), large-scale annotated datasets are required to develop the data-driven AI systems. Two examples are automatic speech recognition (ASR) and natural language understanding (NLU). In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data. The ATCO2 corpus covers 1) data collection and pre-processing, 2) pseudo-annotations of speech data, and 3) extraction of ATC-related named entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set corpus contains 4 hours of ATC speech with manual transcripts and a subset with gold annotations for named-entity recognition (callsign, command, value). 2) The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched with automatic transcripts from an in-domain speech recognizer, contextual information, speaker turn information, signal-to-noise ratio estimate and English language detection score per sample. Both available for purchase through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. 3) The ATCO2-test-set-1h corpus is a one-hour subset from the original test set corpus, that we are offering for free at https://www.atco2.org/data. We expect the ATCO2 corpus will foster research on robust ASR and NLU not only in the field of ATC communications but also in the general research community.Comment: Manuscript under review; The code will be available at https://github.com/idiap/atco2-corpu

Syväoppiminen puhutun kielen tunnistamisessa

This thesis applies deep learning based classification techniques to identify natural languages from speech. The primary motivation behind this thesis is to implement accurate techniques for segmenting multimedia materials by the languages spoken in them. Several existing state-of-the-art, deep learning based approaches are discussed and a subset of the discussed approaches are selected for quantitative experimentation. The selected model architectures are trained on several well-known spoken language identification datasets containing several different languages. Segmentation granularity varies between models, some supporting input audio lengths of 0.2 seconds, while others require 10 second long input to make a language decision. Results from the thesis experiments show that an unsupervised representation of acoustic units, produced by a deep sequence-to-sequence auto encoder, cannot reach the language identification performance of a supervised representation, produced by a multilingual phoneme recognizer. Contrary to most existing results, in this thesis, acoustic-phonetic language classifiers trained on labeled spectral representations outperform phonotactic classifiers trained on bottleneck features of a multilingual phoneme recognizer. More work is required, using transcribed datasets and automatic speech recognition techniques, to investigate why phoneme embeddings did not outperform simple, labeled spectral features. While an accurate online language segmentation tool for multimedia materials could not be constructed, the work completed in this thesis provides several insights for building feasible, modern spoken language identification systems. As a side-product of the experiments performed during this thesis, a free open source spoken language identification software library called "lidbox" was developed, allowing future experiments to begin where the experiments of this thesis end.Tämä diplomityö keskittyy soveltamaan syviä neuroverkkomalleja luonnollisten kielien automaattiseen tunnistamiseen puheesta. Tämän työn ensisijainen tavoite on toteuttaa tarkka menetelmä multimediamateriaalien ositteluun niissä esiintyvien puhuttujen kielien perusteella. Työssä tarkastellaan useampaa jo olemassa olevaa neuroverkkoihin perustuvaa lähestymistapaa, joista valitaan alijoukko tarkempaan tarkasteluun, kvantitatiivisten kokeiden suorittamiseksi. Valitut malliarkkitehtuurit koulutetaan käyttäen eri puhetietokantoja, sisältäen useampia eri kieliä. Kieliosittelun hienojakoisuus vaihtelee käytettyjen mallien mukaan, 0,2 sekunnista 10 sekuntiin, riippuen kuinka pitkän aikaikkunan perusteella malli pystyy tuottamaan kieliennusteen. Diplomityön aikana suoritetut kokeet osoittavat, että sekvenssiautoenkoodaajalla ohjaamattomasti löydetty puheen diskreetti akustinen esitysmuoto ei ole riittävä kielen tunnistamista varten, verrattuna foneemitunnistimen tuottamaan, ohjatusti opetettuun foneemiesitysmuotoon. Tässä työssä havaittiin, että akustisfoneettiset kielentunnistusmallit saavuttavat korkeamman kielentunnistustarkkuuden kuin foneemiesitysmuotoa käyttävät kielentunnistusmallit, mikä eroaa monista kirjallisuudessa esitetyistä tuloksista. Diplomityön tutkimuksia on jatkettava, esimerkiksi litteroituja puhetietokantoja ja puheentunnistusmenetelmiä käyttäen, jotta pystyttäisiin selittämään miksi foneemimallin tuottamalla esitysmuodolla ei saatu parempia tuloksia kuin yksinkertaisemmalla, taajuusspektrin esitysmuodolla. Tämän työn aikana puhutun kielen tunnistaminen osoittautui huomattavasti haasteellisemmaksi kuin mitä työn alussa oli arvioitu, eikä työn aikana onnistuttu toteuttamaan tarpeeksi tarkkaa multimediamateriaalien kielienosittelumenetelmää. Tästä huolimatta, työssä esitetyt lähestymistavat tarjoavat toimivia käytännön menetelmiä puhutun kielen tunnistamiseen tarkoitettujen, modernien järjestelmien rakentamiseksi. Tämän diplomityön sivutuotteena syntyi myös puhutun kielen tunnistamiseen tarkoitettu avoimen lähdekoodin kirjasto nimeltä "lidbox", jonka ansiosta tämän työn kvantitatiivisia kokeita voi jatkaa siitä, mihin ne tämän työn päätteeksi jäivät

Zero-shot personalization of speech foundation models for depressed mood monitoring

The monitoring of depressed mood plays an important role as a diagnostic tool in psychotherapy. An automated analysis of speech can provide a non-invasive measurement of a patient’s affective state. While speech has been shown to be a useful biomarker for depression, existing approaches mostly build population-level models that aim to predict each individual’s diagnosis as a (mostly) static property. Because of inter-individual differences in symptomatology and mood regulation behaviors, these approaches are ill-suited to detect smaller temporal variations in depressed mood. We address this issue by introducing a zero-shot personalization of large speech foundation models. Compared with other personalization strategies, our work does not require labeled speech samples for enrollment. Instead, the approach makes use of adapters conditioned on subject-specific metadata. On a longitudinal dataset, we show that the method improves performance compared with a set of suitable baselines. Finally, applying our personalization strategy improves individual-level fairness

Speech segmentation and speaker diarisation for transcription and translation

This dissertation outlines work related to Speech Segmentation – segmenting an audio recording into regions of speech and non-speech, and Speaker Diarization – further segmenting those regions into those pertaining to homogeneous speakers. Knowing not only what was said but also who said it and when, has many useful applications. As well as providing a richer level of transcription for speech, we will show how such knowledge can improve Automatic Speech Recognition (ASR) system performance and can also benefit downstream Natural Language Processing (NLP) tasks such as machine translation and punctuation restoration. While segmentation and diarization may appear to be relatively simple tasks to describe, in practise we find that they are very challenging and are, in general, ill-defined problems. Therefore, we first provide a formalisation of each of the problems as the sub-division of speech within acoustic space and time. Here, we see that the task can become very difficult when we want to partition this domain into our target classes of speakers, whilst avoiding other classes that reside in the same space, such as phonemes. We present a theoretical framework for describing and discussing the tasks as well as introducing existing state-of-the-art methods and research. Current Speaker Diarization systems are notoriously sensitive to hyper-parameters and lack robustness across datasets. Therefore, we present a method which uses a series of oracle experiments to expose the limitations of current systems and to which system components these limitations can be attributed. We also demonstrate how Diarization Error Rate (DER), the dominant error metric in the literature, is not a comprehensive or reliable indicator of overall performance or of error propagation to subsequent downstream tasks. These results inform our subsequent research. We find that, as a precursor to Speaker Diarization, the task of Speech Segmentation is a crucial first step in the system chain. Current methods typically do not account for the inherent structure of spoken discourse. As such, we explored a novel method which exploits an utterance-duration prior in order to better model the segment distribution of speech. We show how this method improves not only segmentation, but also the performance of subsequent speech recognition, machine translation and speaker diarization systems. Typical ASR transcriptions do not include punctuation and the task of enriching transcriptions with this information is known as ‘punctuation restoration’. The benefit is not only improved readability but also better compatibility with NLP systems that expect sentence-like units such as in conventional machine translation. We show how segmentation and diarization are related tasks that are able to contribute acoustic information that complements existing linguistically-based punctuation approaches. There is a growing demand for speech technology applications in the broadcast media domain. This domain presents many new challenges including diverse noise and recording conditions. We show that the capacity of existing GMM-HMM based speech segmentation systems is limited for such scenarios and present a Deep Neural Network (DNN) based method which offers a more robust speech segmentation method resulting in improved speech recognition performance for a television broadcast dataset. Ultimately, we are able to show that the speech segmentation is an inherently ill-defined problem for which the solution is highly dependent on the downstream task that it is intended for

Robust learning of acoustic representations from diverse speech data

Automatic speech recognition is increasingly applied to new domains. A key challenge is to robustly learn, update and maintain representations to cope with transient acoustic conditions. A typical example is broadcast media, for which speakers and environments may change rapidly, and available supervision may be poor. The concern of this thesis is to build and investigate methods for acoustic modelling that are robust to the characteristics and transient conditions as embodied by such media. The first contribution of the thesis is a technique to make use of inaccurate transcriptions as supervision for acoustic model training. There is an abundance of audio with approximate labels, but training methods can be sensitive to label errors, and their use is therefore not trivial. State-of-the-art semi-supervised training makes effective use of a lattice of supervision, inherently encoding uncertainty in the labels to avoid overfitting to poor supervision, but does not make use of the transcriptions. Existing approaches that do aim to make use of the transcriptions typically employ an algorithm to filter or combine the transcriptions with the recognition output from a seed model, but the final result does not encode uncertainty. We propose a method to combine the lattice output from a biased recognition pass with the transcripts, crucially preserving uncertainty in the lattice where appropriate. This substantially reduces the word error rate on a broadcast task. The second contribution is a method to factorise representations for speakers and environments so that they may be combined in novel combinations. In realistic scenarios, the speaker or environment transform at test time might be unknown, or there may be insufficient data to learn a joint transform. We show that in such cases, factorised, or independent, representations are required to avoid deteriorating performance. Using i-vectors, we factorise speaker or environment information using multi-condition training with neural networks. Specifically, we extract bottleneck features from networks trained to classify either speakers or environments. The resulting factorised representations prove beneficial when one factor is missing at test time, or when all factors are seen, but not in the desired combination. The third contribution is an investigation of model adaptation in a longitudinal setting. In this scenario, we repeatedly adapt a model to new data, with the constraint that previous data becomes unavailable. We first demonstrate the effect of such a constraint, and show that using a cyclical learning rate may help. We then observe that these successive models lend themselves well to ensembling. Finally, we show that the impact of this constraint in an active learning setting may be detrimental to performance, and suggest to combine active learning with semi-supervised training to avoid biasing the model. The fourth contribution is a method to adapt low-level features in a parameter-efficient and interpretable manner. We propose to adapt the filters in a neural feature extractor, known as SincNet. In contrast to traditional techniques that warp the filterbank frequencies in standard feature extraction, adapting SincNet parameters is more flexible and more readily optimised, whilst maintaining interpretability. On a task adapting from adult to child speech, we show that this layer is well suited for adaptation and is very effective with respect to the small number of adapted parameters

Deep Spoken Keyword Spotting:An Overview

Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep learning a few years ago. This has allowed the rapid embedding of deep KWS in a myriad of small electronic devices with different purposes like the activation of voice assistants. Prospects suggest a sustained growth in terms of social use of this technology. Thus, it is not surprising that deep KWS has become a hot research topic among speech scientists, who constantly look for KWS performance improvement and computational complexity reduction. This context motivates this paper, in which we conduct a literature review into deep spoken KWS to assist practitioners and researchers who are interested in this technology. Specifically, this overview has a comprehensive nature by covering a thorough analysis of deep KWS systems (which includes speech features, acoustic modeling and posterior handling), robustness methods, applications, datasets, evaluation metrics, performance of deep KWS systems and audio-visual KWS. The analysis performed in this paper allows us to identify a number of directions for future research, including directions adopted from automatic speech recognition research and directions that are unique to the problem of spoken KWS