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Modern machine learning models for audio tasks often exhibit superior performance on English and other well-resourced languages, primarily due to the abundance of available training data. This disparity leads to an unfair performance gap for low-resource languages, where data collection is both challenging and costly. In this work, we introduce a novel data augmentation technique for speech corpora designed to mitigate this gap. Through comprehensive experiments, we demonstrate that our method significantly improves the performance of automatic speech recognition systems on low-resource languages. Furthermore, we show that our approach outperforms existing augmentation strategies, offering a practical solution for enhancing speech technology in underrepresented linguistic communities.

A.1 Architecture We provide additional details of the V oiceBlock architecture used in our experiments. ReLU activations and hidden size 512. Bottleneck: Our bottleneck consists of two LSTM layers with hidden size 512. We implement a version of V oiceBlock for processing audio streams in real time. Lookahead Buffer: Contains overlapping frames to be used as lookahead.

The hotel and should The hotel should be in the and should have If there is no such hotel, how about one that has Once you find the you want to book it for <4 people> and <2 nights> starting from Y ou are also looking for a . The restaurant should be in the price range and should serve food The restaurant should be Once you find the you want to book a table for at <12:15>(use veiled expression)> on (Background) Once you have made a booking, you do not want to give out your email address for receiving orders.

Abstract--This project presents an AI-based system for tone replication in music production, focusing on predicting EQ parameter settings directly from audio features. Unlike traditional audio-to-audio methods, our approach generates interpretable parameter values--such as EQ band gains--that musicians can further adjust in their workflow. Using a dataset of piano recordings with systematically varied EQ settings, we evaluate both regression and neural network models. Results show that our neural network model achieves highly accurate parameter predictions, with a mean squared error of 0.0216 on multi-band tasks. The proposed system enables practical, flexible, and automated tone matching for music producers, laying the foundation for future extensions to more complex audio effects.

Automatic Speech Recognition (ASR) has transformed daily tasks from video transcription to workplace hiring. ASR systems' growing use warrants robust and standardized auditing approaches to ensure automated transcriptions of high and equitable quality. This is especially critical for people with speech and language disorders (such as aphasia) who may disproportionately depend on ASR systems to navigate everyday life. In this work, we identify three pitfalls in existing standard ASR auditing procedures, and demonstrate how addressing them impacts audit results via a case study of six popular ASR systems' performance for aphasia speakers. First, audits often adhere to a single method of text standardization during data pre-processing, which (a) masks variability in ASR performance from applying different standardization methods, and (b) may not be consistent with how users - especially those from marginalized speech communities - would want their transcriptions to be standardized. Second, audits often display high-level demographic findings without further considering performance disparities among (a) more nuanced demographic subgroups, and (b) relevant covariates capturing acoustic information from the input audio. Third, audits often rely on a single gold-standard metric -- the Word Error Rate -- which does not fully capture the extent of errors arising from generative AI models, such as transcription hallucinations. We propose a more holistic auditing framework that accounts for these three pitfalls, and exemplify its results in our case study, finding consistently worse ASR performance for aphasia speakers relative to a control group. We call on practitioners to implement these robust ASR auditing practices that remain flexible to the rapidly changing ASR landscape.

All languages are peculiar. Some of them are considered more challenging to understand than others. The Finnish Language is known to be a complex language. Also, when languages are used by artists, the pronunciation and meaning might be more tricky to understand. Therefore, we are putting AI to a fun, yet challenging trial: translating a Finnish rap song to text. We will compare the Faster Whisperer algorithm and YouTube's internal speech-to-text functionality. The reference truth will be Finnish rap lyrics, which the main author's little brother, Mc Timo, has written. Transcribing the lyrics will be challenging because the artist raps over synth music player by Syntikka Janne. The hallucination level and mishearing of AI speech-to-text extractions will be measured by comparing errors made against the original Finnish lyrics. The error function is informal but still works for our case.