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Register a new userOn Generative Spoken Language Modeling from Raw Audio
على النمذجة اللغة المنطوقة من الصوت من الصوت الخام
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مجرر أن نقدم نمذجة اللغة المنطوقة الإندنية، ومهمة تعلم الخصائص الصوتية واللغوية للغة من الصوت الخام (لا توجد نص، لا ملصقات)، ومجموعة من المقاييس لتقييم التمثيلات المستفادة تلقائيا على المستويات الصوتية واللغوية لكلا الترميزوالجيل.أنشأنا أنظمة أساسية تتكون من تشفير خطاب منفصل (وحدات النص الزائفة العائدين)، وهي نموذج لغة تابعة له (تدرب على النص الزائف)، وحديد فك الكلام (توليد موجة موجلة من النص الزائف) جميعها مدربة دون إشراف والتحقق من صحةالمقاييس المقترحة مع التقييم البشري.عبر 3 تشفير الكلام (CPC، WAV2VEC 2.0، HUBERT)، نجد أن عدد الوحدات المنفصلة (50 أو 100 أو أو 200) مسائل في طريقة تعتمد على المهام والتشييد، وأن بعض المجموعات نهج النظم القائمة على النصوص.1.
Abstract We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. We set up baseline systems consisting of a discrete speech encoder (returning pseudo-text units), a generative language model (trained on pseudo- text), and a speech decoder (generating a waveform from pseudo-text) all trained without supervision and validate the proposed metrics with human evaluation. Across 3 speech encoders (CPC, wav2vec 2.0, HuBERT), we find that the number of discrete units (50, 100, or 200) matters in a task-dependent and encoder- dependent way, and that some combinations approach text-based systems.1
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The lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU). As key tasks like intent classification and slot filling require abundant training data, it is desirable to reuse existi
ng data in high-resource languages to develop models for low-resource scenarios. We introduce xSID, a new benchmark for cross-lingual (x) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect. To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. We study two setups which differ by type and language coverage of the pre-trained embeddings. Our results show that jointly learning the main tasks with masked language modeling is effective for slots, while machine translation transfer works best for intent classification.
The sound is an essential component of multimedia, and due to the needto be used in
many life applications such as television broadcasting andcommunication programs, so it
was necessary for the existence of audio signal processing techniquessuch as
compressing,
improving, and noisereduction.
Data compression process aims to reduce the bit rate used, by doing encoding
information using fewer bits than the original representation for transmitting and storing.
By this process,the unnecessary information is determined and removed, that means it
gives the compressed information for useable compression, which we need as a
fundamental, not the minutest details.
This research aims to study how to process sound and musical signal. It's a process
that consists of a wide range of applications like coding and digital compression for the
effective transport and storage on mobile phones and portable music players, modeling and
reproduction of the sound of musical instruments and music halls and the harmonics of
digital music, editing digital music, and classification of music content, and other things.
Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languag
es, the augmented data sets are often noisy, and thus impede the performance of SLU models. In this paper we focus on mitigating noise in augmented data. We develop a denoising training approach. Multiple models are trained with data produced by various augmented methods. Those models provide supervision signals to each other. The experimental results show that our method outperforms the existing state of the art by 3.05 and 4.24 percentage points on two benchmark datasets, respectively. The code will be made open sourced on github.
تعرض المحاضرة شرح عن علم البيانات وعلاقته بعلم الإحصاء والتعلم الآلي وحالتين دراسيتين عن دور عالم البيانات في تصميم حلول تعتمد على استخراج المعرفة من حجم كبير من البيانات المتوفرة, كما يتم عرض أهم المهام في المؤتمرات العلمية التي يمكن المشاركة بها لطلاب المعلوماتية المهتمين بهذا المجال
In this paper, we propose a new method to embed digital
watermarking in audio files, using Discrete Wavelet Transform
(DWT) and the way to extract the watermark data.
The method efficiency is measured using Peak Signal –to-Noise
Ratio (PSNR) , No
rmalized Correlation Coefficient (NC). The
advantage of our method is the robustness against several attacks
and compression.
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