Self-supervised learning has been used to leverage unlabelled data, improving
accuracy and generalisation of speech systems through the training of
representation models. While many recent works have sought to produce effective
representations across a variety of acoustic domains, languages, modalities and
even simultaneous speakers, these studies have all been limited to
single-channel audio recordings. This paper presents Spatial HuBERT, a
self-supervised speech representation model that learns both acoustic and
spatial information pertaining to a single speaker in a potentially noisy
environment by using multi-channel audio inputs. Spatial HuBERT learns
representations that outperform state-of-the-art single-channel speech
representations on a variety of spatial downstream tasks, particularly in
reverberant and noisy environments. We also demonstrate the utility of the
representations learned by Spatial HuBERT on a speech localisation downstream
task. Along with this paper, we publicly release a new dataset of 100 000
simulated first-order ambisonics room impulse responses.

Spatial HuBERT 是一种自我监督的语音表示模型，通过使用多通道音频输入学习单个说话者在潜在嘈杂环境中的声学和空间信息，可以在多种空间下游任务中优于最先进的单通道语音表示，在混响和嘈杂环境中表现出色。

Spatial HuBERT：基于多通道音频的自监督单讲话者空间语音表征学习

Spatial HuBERT: Self-supervised Spatial Speech Representation Learning  for a Single Talker from Multi-channel Audio

Speech systems are sensitive to accent variations. This is especially
challenging in the Indian context, with an abundance of languages but a dearth
of linguistic studies characterising pronunciation variations. The growing
number of L2 English speakers in India reinforces the need to study accents and
L1-L2 interactions. We investigate the accents of Indian English (IE) speakers
and report in detail our observations, both specific and common to all regions.
In particular, we observe the phonemic variations and phonotactics occurring in
the speakers' native languages and apply this to their English pronunciations.
We demonstrate the influence of 18 Indian languages on IE by comparing the
native language pronunciations with IE pronunciations obtained jointly from
existing literature studies and phonetically annotated speech of 80 speakers.
Consequently, we are able to validate the intuitions of Indian language
influences on IE pronunciations by justifying pronunciation rules from the
perspective of Indian language phonology. We obtain a comprehensive description
in terms of universal and region-specific characteristics of IE, which
facilitates accent conversion and adaptation of existing ASR and TTS systems to
different Indian accents.

研究印度英语说话者的口音，包括其母语语音的影响，并系统性地描述了印度英语的特点，以便于在自动语音识别和语音合成系统中实现印度口音的转换和适应。

印度本土语音素对 L2 英语发音影响的研究

An Investigation of Indian Native Language Phonemic Influences on L2 English Pronunciations

People may be puzzled by the fact that voice over recordings data sets exist
in addition to Text-to-Speech (TTS), Synthesis system advancements, albeit this
is not the case. The goal of this study is to explain the relevance of TTS as
well as the data preparation procedures. TTS relies heavily on recorded data
since it can have a substantial influence on the outcomes of TTS modules.
Furthermore, whether the domain is specialized or general, appropriate data
should be developed to address all predicted language variants and domains.
Different recording methodologies, taking into account quality and behavior,
may also be advantageous in the development of the module. In light of the lack
of Arabic language in present synthesizing systems, numerous variables that
impact the flow of recorded utterances are being considered in order to
manipulate an Arabic TTS module. In this study, two viewpoints will be
discussed: linguistics and the creation of high-quality recordings for TTS. The
purpose of this work is to offer light on how ground-truth utterances may
influence the evolution of speech systems in terms of naturalness,
intelligibility, and understanding. Well provide voice actor specs as well as
data specs that will assist both voice actors and voice coaches in the studio
as well as the annotators who will be evaluating the audios.

该研究旨在解释 TTS 的相关性和数据准备过程，讨论语言学和高质量记录的创作，提供语音演员和数据规范，评估音频并影响语音系统的演变。