SpeechBrain is an open-source Conversational AI toolkit based on PyTorch, focused particularly on speech processing tasks such as speech recognition, speech enhancement, speaker recognition, text-to-speech, and much more. It promotes transparency and replicability by releasing both the pre-trained models and the complete recipes of code and algorithms required for training them. This paper presents SpeechBrain 1.0, a significant milestone in the evolution of the toolkit, which now has over 200 recipes for speech, audio, and language processing tasks, and more than 100 models available on Hugging Face. SpeechBrain 1.0 introduces new technologies to support diverse learning modalities, Large Language Model (LLM) integration, and advanced decoding strategies, along with novel models, tasks, and modalities. It also includes a new benchmark repository, offering researchers a unified platform for evaluating models across diverse tasks.

End-to-End speech recognition has become the center of attention for speech recognition research, but Hybrid Hidden Markov Model Deep Neural Network (HMM/DNN) -systems remain a competitive approach in terms of performance. End-to-End models may be better at very large data scales, and HMM / DNN-systems may have an advantage in low-resource scenarios, but the thousand-hour scale is particularly interesting for comparisons. At that scale experiments have not been able to conclusively demonstrate which approach is best, or if the heterogeneous approaches yield similar results. In this work, we work towards answering that question for Attention-based Encoder-Decoder models compared with HMM / DNN-systems. We present two simple experimental design principles, and how to build systems adhering to those principles. We demonstrate how those principles remove confounding variables related to both data, and neural architecture and training. We apply the principles in a set of experiments on three diverse thousand-hour-scale tasks. In our experiments, the HMM / DNN-systems yield equal or better results in almost all cases.

Self-supervised speech models, such as the wav2vec2, have become extremely popular in the past few years. Their main appeal is that after their pre-training on a large amount of audio, they require only a small amount of supervised, finetuning data to achieve outstanding results. Despite their immense success, very little is understood about the pre-trained models and how finetuning changes them. In this work, we take the first steps towards a better understanding of wav2vec2 systems using model interpretation tools such as visualization and latent embedding clustering. Through our analysis, we gain new insights into the abilities of the pre-trained networks and the effect that finetuning has on them. We demonstrate that the clusters learned by the pre-trained model are just as important a factor as the supervised training data distribution in determining the accuracy of the finetuned system, which could aid us in selecting the most suitable pre-trained model for the supervised data.

Funding Information: We are grateful for the Academy of Finland project funding, numbers: 337073, 345790. We acknowledge the computational resources provided by the Aalto Science-IT project. Publisher Copyright: Copyright © 2022 ISCA.

Funding Information: This work is part of Digitala project which is funded by the Academy of Finland (grant numbers 322619, 322625, 322965). The computational resources were provided by Aalto ScienceIT. Funding Information: This work is part of Digitala project which is funded by the Academy of Finland (grant numbers 322619, 322625, 322965). The computational resources were provided by Aalto Scien-ceIT. Publisher Copyright: Copyright © 2021 ISCA.

In speaker-aware training, a speaker embedding is appended to DNN input features. This allows the DNN to effectively learn representations, which are robust to speaker variability.We apply speaker-aware training to attention-based end-to-end speech recognition. We show that it can improve over a purely end-to-end baseline. We also propose speaker-aware training as a viable method to leverage untranscribed, speaker annotated data.We apply state-of-the-art embedding approaches, both i-vectors and neural embeddings, such as x-vectors. We experiment with embeddings trained in two conditions: on the fixed ASR data, and on a large untranscribed dataset. We run our experiments on the TED-LIUM and Wall Street Journal datasets. No embedding consistently outperforms all others, but in many settings neural embeddings outperform i-vectors.

| openaire: EC/H2020/780069/EU//MeMAD

Abstract Multimodal machine translation involves drawing information from more than one modality, based on the assumption that the additional modalities will contain useful alternative views of the input data. The most prominent tasks in this area are spoken language translation, image-guided translation, and video-guided translation, which exploit audio and visual modalities, respectively. These tasks are distinguished from their monolingual counterparts of speech recognition, image captioning, and video captioning by the requirement of models to generate outputs in a different language. This survey reviews the major data resources for these tasks, the evaluation campaigns concentrated around them, the state of the art in end-to-end and pipeline approaches, and also the challenges in performance evaluation. The paper concludes with a discussion of directions for future research in these areas: the need for more expansive and challenging datasets, for targeted evaluations of model performance, and for multimodality in both the input and output space.

In this paper, we present SphereDiar, a speaker diarization system composed of three novel subsystems: the Sphere-Speaker (SS) neural network, designed for speaker embedding extraction, a segmentation method called Homogeneity Based Segmentation (HBS) and a clustering algorithm called Top Two Silhouettes (Top2S). The system is evaluated on a set of over 200 manually transcribed multiparty meetings. The evaluation reveals that the system can be further simplified by omitting the use of HBS. Furthermore, we illustrate that SphereDiar achieves state-of-the-art results with two different meeting data sets.