End-to-end named entity recognition for spoken Finnish

Named entity recognition is a natural language processing task in which the system tries to find named entities and classify them in predefined categories. The categories can vary, depending on the domain in which they are going to be used but some of the most common include: person, location, organization, date and product. Named entity recognition is an integral part of other large natural language processing tasks, such as information retrieval, text summarization, machine translation, and question answering. Doing named entity recognition is a difficult task due to the lack of annotated data for certain languages or domains. Named entity ambiguity is another challenging aspect that arises when doing named entity recognition. Often times, a word can represent a person, organization, product, or any other category, depending on the context it appears in. Spoken data, which can be the output of a speech recognition system, imposes additional challenges to the named entity recognition system. Named entities are often capitalized and the system learns to rely on capitalization in order to detect the entities, which is neglected in the speech recognition output. The standard way of doing named entity recognition from speech involves a pipeline approach of two systems. First, a speech recognition system transcribes the speech and generates the transcripts, after which a named entity recognition system annotates the transcripts with the named entities. Since the speech recognition system is not perfect and makes errors, those errors are propagated to the named entity recognition system, which is hard to recover from. In this thesis, we present two approaches of doing named entity recognition from Finnish speech in an end-to-and manner, where one system generates the transcripts and the annotations. We will explore the strengths and weaknesses of both approaches and see how they compare to the standard pipeline approach

Topic Identification For Spontaneous Speech: Enriching Audio Features With Embedded Linguistic Information

Traditional topic identification solutions from audio rely on an automatic speech recognition system (ASR) to produce transcripts used as input to a text-based model. These approaches work well in high-resource scenarios, where there are sufficient data to train both components of the pipeline. However, in low-resource situations, the ASR system, even if available, produces low-quality transcripts, leading to a bad text-based classifier. Moreover, spontaneous speech containing hesitations can further degrade the performance of the ASR model. In this paper, we investigate alternatives to the standard text-only solutions by comparing audio-only and hybrid techniques of jointly utilising text and audio features. The models evaluated on spontaneous Finnish speech demonstrate that purely audio-based solutions are a viable option when ASR components are not available, while the hybrid multi-modal solutions achieve the best results.Comment: Accepted to EUSIPCO 202

Trained models for the paper: Topic Identification for Spontaneous Speech: Enriching Audio Features with Embedded Linguistic Information

<p>In the repository you can find the trained models used in the paper: Topic Identification for Spontaneous Speech: Enriching Audio Features with Embedded Linguistic Information</p&gt

Attention-Based End-To-End Named Entity Recognition From Speech

| openaire: EC/H2020/780069/EU//MeMADNamed entities are heavily used in the field of spoken language understanding, which uses speech as an input. The standard way of doing named entity recognition from speech involves a pipeline of two systems, where first the automatic speech recognition system generates the transcripts, and then the named entity recognition system produces the named entity tags from the transcripts. In such cases, automatic speech recognition and named entity recognition systems are trained independently, resulting in the automatic speech recognition branch not being optimized for named entity recognition and vice versa. In this paper, we propose two attention-based approaches for extracting named entities from speech in an end-to-end manner, that show promising results. We compare both attention-based approaches on Finnish, Swedish, and English data sets, underlining their strengths and weaknesses.Peer reviewe

Advancing Audio Emotion and Intent Recognition with Large Pre-Trained Models and Bayesian Inference

Large pre-trained models are essential in paralinguistic systems, demonstrating effectiveness in tasks like emotion recognition and stuttering detection. In this paper, we employ large pre-trained models for the ACM Multimedia Computational Paralinguistics Challenge, addressing the Requests and Emotion Share tasks. We explore audio-only and hybrid solutions leveraging audio and text modalities. Our empirical results consistently show the superiority of the hybrid approaches over the audio-only models. Moreover, we introduce a Bayesian layer as an alternative to the standard linear output layer. The multimodal fusion approach achieves an 85.4% UAR on HC-Requests and 60.2% on HC-Complaints. The ensemble model for the Emotion Share task yields the best value of .614. The Bayesian wav2vec2 approach, explored in this study, allows us to easily build ensembles, at the cost of fine-tuning only one model. Moreover, we can have usable confidence values instead of the usual overconfident posterior probabilities.Peer reviewe

Wav2vec2-based Paralinguistic Systems to Recognise Vocalised Emotions and Stuttering

With the rapid advancement in automatic speech recognition and natural language understanding, a complementary field (paralin- guistics) emerged, focusing on the non-verbal content of speech. The ACM Multimedia 2022 Computational Paralinguistics Challenge introduced several exciting tasks of this field. In this work, we focus on tackling two Sub-Challenges using modern, pre-trained models called wav2vec2. Our experimental results demonstrated that wav2vec2 is an excellent tool for detecting the emotions behind vocalisations and recognising different types of stutterings. Albeit they achieve outstanding results on their own, our results demonstrated that wav2vec2-based systems could be further improved by ensembling them with other models. Our best systems outperformed the competition baselines by a considerable margin, achieving an unweighted average recall of 44.0 (absolute improvement of 6.6% over baseline) on the Vocalisation Sub-Challenge and 62.1 (absolute improvement of 21.7% over baseline) on the Stuttering Sub-Challenge.Peer reviewe

Lahjoita puhetta : a large-scale corpus of spoken Finnish with some benchmarks

Publisher Copyright: © 2022, The Author(s).In 2020-2021, the Donate Speech campaign gathered approximately 3600 h of ordinary, colloquial Finnish speech for the Lahjoita puhetta (Donate Speech) corpus, which includes over twenty thousand speakers from all the regions of Finland and from all age brackets. The goals of the collection were to create a representative, large-scale resource of spontaneous spoken Finnish to accelerate the development of language technology and speech-based services.Peer reviewe