Chandan Reddy

Xavier Gitiaux, Aditya Khant, Ebrahim Beyrami et al.

Noise suppression models running in production environments are commonly trained on publicly available datasets. However, this approach leads to regressions due to the lack of training/testing on representative customer data. Moreover, due to privacy reasons, developers cannot listen to customer content. This `ears-off' situation motivates augmenting existing datasets in a privacy-preserving manner. In this paper, we present Aura, a solution to make existing noise suppression test sets more challenging and diverse while being sample efficient. Aura is `ears-off' because it relies on a feature extractor and a metric of speech quality, DNSMOS P.835, both pre-trained on data obtained from public sources. As an application of Aura, we augment the INTERSPEECH 2021 DNS challenge by sampling audio files from a new batch of data of 20K clean speech clips from Librivox mixed with noise clips obtained from AudioSet. Aura makes the existing benchmark test set harder by 0.27 in DNSMOS P.835 OVLR (7%), 0.64 harder in DNSMOS P.835 SIG (16%), increases diversity by 31%, and achieves a 26% improvement in Spearman's rank correlation coefficient (SRCC) compared to random sampling. Finally, we open-source Aura to stimulate research of test set development.

Mohammad Beigi, Ying Shen, Parshin Shojaee et al.

Despite the remarkable capabilities of large language models, current training paradigms inadvertently foster \textit{sycophancy}, i.e., the tendency of a model to agree with or reinforce user-provided information even when it's factually incorrect. To address this challenge, we introduce \textbf{SMART} (Sycophancy Mitigation through Adaptive Reasoning Trajectories), which reframes sycophancy as a \textit{reasoning optimization problem} rather than an output alignment issue. SMART is a two-stage framework comprising: (1) Uncertainty-Aware Adaptive Monte Carlo Tree Search (UA-MCTS), which dynamically adjusts model exploration based on state-level uncertainty to collect high-quality, diverse reasoning trajectories alongside both stepwise progress and final outcome rewards; and (2) progress-based reinforcement learning, which fine-tunes the model using the collected trajectories and reward signals to reinforce effective reasoning patterns. Through extensive experiments, we show that SMART significantly reduces sycophantic behavior while preserving strong performance on out-of-distribution inputs and maintaining general capabilities. These results underscore the importance of optimizing internal reasoning mechanisms to build more truthful and aligned AI assistants.

Anderson R. Avila, Hannes Gamper, Chandan Reddy et al.

Estimating the perceived quality of an audio signal is critical for many multimedia and audio processing systems. Providers strive to offer optimal and reliable services in order to increase the user quality of experience (QoE). In this work, we present an investigation of the applicability of neural networks for non-intrusive audio quality assessment. We propose three neural network-based approaches for mean opinion score (MOS) estimation. We compare our results to three instrumental measures: the perceptual evaluation of speech quality (PESQ), the ITU-T Recommendation P.563, and the speech-to-reverberation energy ratio. Our evaluation uses a speech dataset contaminated with convolutive and additive noise, labeled using a crowd-based QoE evaluation, evaluated with Pearson correlation with MOS labels, and mean-squared-error of the estimated MOS. Our proposed approaches outperform the aforementioned instrumental measures, with a fully connected deep neural network using Mel-frequency features providing the best correlation (0.87) and the lowest mean squared error (0.15)