Neural networks for language modeling have been proven effective on several sub-tasks of natural language processing. Training deep language models, however, is time-consuming and computationally intensive. Pre-trained language models such as BERT are thus appealing since (1) they yielded state-of-the-art performance, and (2) they offload practitioners from the burden of preparing the adequate resources (time, hardware, and data) to train models. Nevertheless, because pre-trained models are generic, they may underperform on specific domains. In this study, we investigate the case of multi-class text classification, a task that is relatively less studied in the literature evaluating pre-trained language models. Our work is further placed under the industrial settings of the financial domain. We thus leverage generic benchmark datasets from the literature and two proprietary datasets from our partners in the financial technological industry. After highlighting a challenge for generic pre-trained models (BERT, DistilBERT, RoBERTa, XLNet, XLM) to classify a portion of the financial document dataset, we investigate the intuition that a specialized pre-trained model for financial documents, such as FinBERT, should be leveraged. Nevertheless, our experiments show that the FinBERT model, even with an adapted vocabulary, does not lead to improvements compared to the generic BERT models.

/pdf/a-comparison-of-pre-trained-language-models-for-multi-class-55k1nmdhru.pdf

A Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain

Searching, retrieving, and arranging text in ever-larger document collections necessitate more efficient information processing algorithms. Document categorization is a crucial component of various information processing systems for supervised learning. As the quantity of documents grows, the performance of classic supervised classifiers has deteriorated because of the number of document categories. Assigning documents to a predetermined set of classes is called text classification. It is utilized extensively in a wide range of data-intensive applications. However, the fact that real-world implementations of these models are plagued with shortcomings begs for more investigation. Imbalanced datasets hinder the most prevalent high-performance algorithms. In this paper, we propose an approach name multi-class Convolutional Neural Network (MCNN)-Long Short-Time Memory (LSTM), which combines two deep learning techniques, Convolutional Neural Network (CNN) and Long Short-Time Memory, for text classification in news data. CNN’s are used as feature extractors for the LSTMs on text input data and have the spatial structure of words in a sentence, paragraph, or document. The dataset is also imbalanced, and we use the Tomek-Link algorithm to balance the dataset and then apply our model, which shows better performance in terms of F1-score (98%) and Accuracy (99.71%) than the existing works. The combination of deep learning techniques used in our approach is ideal for the classification of imbalanced datasets with underrepresented categories. Hence, our method outperformed other machine learning algorithms in text classification by a large margin. We also compare our results with traditional machine learning algorithms in terms of imbalanced and balanced datasets.

MCNN-LSTM: Combining CNN and LSTM to Classify Multi-Class Text in Imbalanced News Data

ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions. Compared to previous challenges, the ASVspoof 5 database is built from crowdsourced data collected from a vastly greater number of speakers in diverse acoustic conditions. Attacks, also crowdsourced, are generated and tested using surrogate detection models, while adversarial attacks are incorporated for the first time. New metrics support the evaluation of spoofing-robust automatic speaker verification (SASV) as well as stand-alone detection solutions, i.e., countermeasures without ASV. We describe the two challenge tracks, the new database, the evaluation metrics, baselines, and the evaluation platform, and present a summary of the results. Attacks significantly compromise the baseline systems, while submissions bring substantial improvements. 

pdf/asvspoof-5-crowdsourced-speech-data-deepfakes-and-38qxee33otx6.pdf

ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks
  at Scale

Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features

In this work, we propose a speaker anonymization pipeline that leverages high quality automatic speech recognition and synthesis systems to generate speech conditioned on phonetic transcriptions and anonymized speaker embeddings. Using phones as the intermediate representation ensures near complete elim-ination of speaker identity information from the input while preserving the original phonetic content as much as possible. Our experimental results on LibriSpeech and VCTK corpora reveal two key ﬁndings: 1) although automatic speech recognition produces imperfect transcriptions, our neural speech synthesis system can handle such errors, making our system fea-sible and robust, and 2) combining speaker embeddings from different resources is beneﬁcial and their appropriate normalization is crucial. Overall, our ﬁnal best system outperforms sig-niﬁcantly the baselines provided in the Voice Privacy Challenge 2020 in terms of privacy robustness against a lazy-informed attacker while maintaining high intelligibility and naturalness of the anonymized speech.

Speaker Anonymization with Phonetic Intermediate Representations

Prosody is closely linked to the identity of a speaker, leading to individual pitch and intonation patterns. Therefore, it is challenging in speaker anonymization to generate speech utterances that both keep the original audio’s main prosodic structure and preserve the speaker’s privacy. In this paper, we present a system that extends a speech-to-text-to-speech anonymization pipeline with prosody cloning and show how to control the cloning by multiplying pitch and energy sequences with random offset values. Using automatic and human evaluation, we find this combination to successfully overcome the privacy-utility trade-off for prosody by achieving high privacy and high pitch correlation scores. At the same time, the anonymized utterances prove to reproduce the original voice distinctiveness and content with high intelligibility and only a small loss in naturalness, making them suitable for downstream applications. 

Prosody Is Not Identity: A Speaker Anonymization Approach Using Prosody Cloning

This paper discusses methods to improve the performance of text classification on data that is difficult to classify due to a large number of unbalanced classes with noisy examples. A variety of features are tested, in combination with three different neural-network-based methods with increasing complexity. The classifiers are applied to a songtext–artist dataset which is large, unbalanced and noisy. We come to the conclusion that substantial improvement can be obtained by removing unbalancedness and sparsity from the data. This fulfils a classification task unsatisfactorily—however, with contemporary methods, it is a practical step towards fairly satisfactory results.

Multiclass Text Classification on Unbalanced, Sparse and Noisy Data

The cloning of a speaker's voice using an untranscribed reference sample is one of the great advances of modern neural text-to-speech (TTS) methods. Approaches for mimicking the prosody of a transcribed reference audio have also been proposed recently. In this work, we bring these two tasks together for the first time through utterance level normalization in conjunction with an utterance level speaker embedding. We further introduce a lightweight aligner for extracting fine-grained prosodic features, that can be finetuned on individual samples within seconds. We show that it is possible to clone the voice of a speaker as well as the prosody of a spoken reference independently without any degradation in quality and high similarity to both original voice and prosody, as our objective evaluation and human study show. All of our code and trained models are available, alongside static and interactive demos.

Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech

For our contribution to the Blizzard Challenge 2023, we improved on the system we submitted to the Blizzard Challenge 2021. Our approach entails a rule-based text-to-phoneme processing system that includes rule-based disambiguation of homographs in the French language. It then transforms the phonemes to spectrograms as intermediate representations using a fast and efficient non-autoregressive synthesis architecture based on Conformer and Glow. A GAN based neural vocoder that combines recent state-of-the-art approaches converts the spectrogram to the final wave. We carefully designed the data processing, training, and inference procedures for the challenge data. Our system identifier is G. Open source code and demo are available.

pdf/the-ims-toucan-system-for-the-blizzard-challenge-2023-51wchpmu9i.pdf

The IMS Toucan System for the Blizzard Challenge 2023

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