A study of four galactic small H II regions: searching for spontaneous and sequential star formation scenarios

This thesis describes observational studies of four small star-forming H II regions (KR 7, KR 81, KR 120 and KR 140) and star-formation scenario associated with the Young Stellar Objects (YSOs) in each region. In addition to that, we also present an analysis of HCO+ (J=3→2) and H13CO+ (J=3→2) observations of the Massive (M ∼ 20 M⊙) submillimeter/infrared source IRAS 01202+6133 located on the periphery of the H II region. In this research, we improved existing 1-D radiative transfer model for a collapsing core that happens in the early phase − Class I protostar − of star formation. The molecular gas surrounding an H II region is thought to be a place where star formation can be induced. We selected four small H II region in order to minimize the feedbacks and dynamics from multiple exciting sources. These regions are very young and ionized by the single O or B spectral type stars. A space based telescope Wide-field Infrared Survey Explorer (WISE) used for identi- fying and classifying the YSOs population surrounding a sample of H II regions. First, we used WISE data from AllWISE catalog with some constrains such as spatial coordinates, signal-to-noise ratio and contaminations. After we retrieved sources from catalog in each region, we classified YSOs with two different methods; color-color diagram and spectral index (α). Based on the color-color diagram using WISE 3.4 μm, 4.6 μm and 12 μm bands, we classified the YSOs as Class I, Class II and using 3.4 μm, 4.6 μm and 22 μm, we were able to classify Transition Disks and Class III YSOs. 2MASS and WISE combined color-color diagram also used in order to compare the classification only use of WISE color-color diagram. Considering a reddening effect from 2MASS Ks band, the classification from both WISE only and 2MASS, WISE combined color-colordiagram. A spectral index (α) also can be used as classifying YSOs. Based on the WISE magnitude, spectral index (α) can be derived from the flux of mid-infrared observation provides a quick and easy way to classify YSOs. However, this method is less accurate then color-color diagrams because we cannot filter out all the contaminants and lack of data sets. Therefore, color-color diagrams can be used a primary methods to identify and classify YSOs. Based on the spatial distribution and number ratio of YSOs, a sequential star-formation scenario is dominant for KR 7, KR 81 and KR 120. In KR 140 region both a sequential star-formation scenario and a spontaneous star-formation scenario can be used to explain the origin of star-forming scenario. Next, we observed HCO+ line profile to investigate the infall motion of the protostar in KR 120 region. The HCO+ line profile has a classic blue-asymmetric shape with the optically thin H13CO+ line peaking at the position expected if the HCO+ line arises from a combination of self-absorption and infall motion. We have modified existing analytic radiative transfer models to allow for the fitting of submm line profiles that have both self-absorption features and optically thin wings and applied these models to our HCO+ spectrum of IRAS 01202+6133 in KR 120. We conclude that it is a young Class I YSO with a substantial envelope undergoing slow infall and having some outflow motions. The young age of the H II region rules out a ”collect and collapse” scenario. While we cannot eliminate the possibility that IRAS 01202+6133 formed spontaneously at its current location, considering its early evolutionary state and its proximity to the H II region, we think that the formation of IRAS 01202+6133 was triggered by the expansion of KR 120 (Sh 2-187)

Grad-StyleSpeech: Any-speaker Adaptive Text-to-Speech Synthesis with Diffusion Models

There has been a significant progress in Text-To-Speech (TTS) synthesis technology in recent years, thanks to the advancement in neural generative modeling. However, existing methods on any-speaker adaptive TTS have achieved unsatisfactory performance, due to their suboptimal accuracy in mimicking the target speakers' styles. In this work, we present Grad-StyleSpeech, which is an any-speaker adaptive TTS framework that is based on a diffusion model that can generate highly natural speech with extremely high similarity to target speakers' voice, given a few seconds of reference speech. Grad-StyleSpeech significantly outperforms recent speaker-adaptive TTS baselines on English benchmarks. Audio samples are available at https://nardien.github.io/grad-stylespeech-demo.Comment: ICASSP 202

KALA: Knowledge-Augmented Language Model Adaptation

Pre-trained language models (PLMs) have achieved remarkable success on various natural language understanding tasks. Simple fine-tuning of PLMs, on the other hand, might be suboptimal for domain-specific tasks because they cannot possibly cover knowledge from all domains. While adaptive pre-training of PLMs can help them obtain domain-specific knowledge, it requires a large training cost. Moreover, adaptive pre-training can harm the PLM's performance on the downstream task by causing catastrophic forgetting of its general knowledge. To overcome such limitations of adaptive pre-training for PLM adaption, we propose a novel domain adaption framework for PLMs coined as Knowledge-Augmented Language model Adaptation (KALA), which modulates the intermediate hidden representations of PLMs with domain knowledge, consisting of entities and their relational facts. We validate the performance of our KALA on question answering and named entity recognition tasks on multiple datasets across various domains. The results show that, despite being computationally efficient, our KALA largely outperforms adaptive pre-training. Code is available at: https://github.com/Nardien/KALA/.Comment: NAACL 202

ZET-Speech: Zero-shot adaptive Emotion-controllable Text-to-Speech Synthesis with Diffusion and Style-based Models

Emotional Text-To-Speech (TTS) is an important task in the development of systems (e.g., human-like dialogue agents) that require natural and emotional speech. Existing approaches, however, only aim to produce emotional TTS for seen speakers during training, without consideration of the generalization to unseen speakers. In this paper, we propose ZET-Speech, a zero-shot adaptive emotion-controllable TTS model that allows users to synthesize any speaker's emotional speech using only a short, neutral speech segment and the target emotion label. Specifically, to enable a zero-shot adaptive TTS model to synthesize emotional speech, we propose domain adversarial learning and guidance methods on the diffusion model. Experimental results demonstrate that ZET-Speech successfully synthesizes natural and emotional speech with the desired emotion for both seen and unseen speakers. Samples are at https://ZET-Speech.github.io/ZET-Speech-Demo/.Comment: Accepted by INTERSPEECH 202