How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning

• URL: http://arxiv.org/abs/2005.11838v2
• Date: Tue, 21 Jul 2020 18:20:24 GMT
• Title: How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning
• Authors: Aviad Elyashar, Rami Puzis, Michael Fire
• Abstract summary: SpokenName2Vec is a novel and generic approach which addresses the similar name suggestion problem. The proposed approach was demonstrated on a large-scale dataset consisting of 250,000 forenames. The performance of the proposed approach was found to be superior to 10 other algorithms evaluated in this study.
• Score: 4.769747792846004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Searching for information about a specific person is an online activity frequently performed by many users. In most cases, users are aided by queries containing a name and sending back to the web search engines for finding their will. Typically, Web search engines provide just a few accurate results associated with a name-containing query. Currently, most solutions for suggesting synonyms in online search are based on pattern matching and phonetic encoding, however very often, the performance of such solutions is less than optimal. In this paper, we propose SpokenName2Vec, a novel and generic approach which addresses the similar name suggestion problem by utilizing automated speech generation, and deep learning to produce spoken name embeddings. This sophisticated and innovative embeddings captures the way people pronounce names in any language and accent. Utilizing the name pronunciation can be helpful for both differentiating and detecting names that sound alike, but are written differently. The proposed approach was demonstrated on a large-scale dataset consisting of 250,000 forenames and evaluated using a machine learning classifier and 7,399 names with their verified synonyms. The performance of the proposed approach was found to be superior to 10 other algorithms evaluated in this study, including well used phonetic and string similarity algorithms, and two recently proposed algorithms. The results obtained suggest that the proposed approach could serve as a useful and valuable tool for solving the similar name suggestion problem.

Related papers

• VacancySBERT: the approach for representation of titles and skills for semantic similarity search in the recruitment domain [0.0]
  The paper focuses on deep learning semantic search algorithms applied in the HR domain. The aim of the article is developing a novel approach to training a Siamese network to link the skills mentioned in the job ad with the title.
  arXiv  Detail & Related papers  (2023-07-31T13:21:15Z)
• SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings [76.87664008338317]
  Contextual spelling correction models are an alternative to shallow fusion to improve automatic speech recognition. We propose a novel algorithm for candidate retrieval based on misspelled n-gram mappings. Experiments on Spoken Wikipedia show 21.4% word error rate improvement compared to a baseline ASR system.
  arXiv  Detail & Related papers  (2023-06-04T10:00:12Z)
• Disambiguation of Company names via Deep Recurrent Networks [101.90357454833845]
  We propose a Siamese LSTM Network approach to extract -- via supervised learning -- an embedding of company name strings. We analyse how an Active Learning approach to prioritise the samples to be labelled leads to a more efficient overall learning pipeline.
  arXiv  Detail & Related papers  (2023-03-07T15:07:57Z)
• Unified Functional Hashing in Automatic Machine Learning [58.77232199682271]
  We show that large efficiency gains can be obtained by employing a fast unified functional hash. Our hash is "functional" in that it identifies equivalent candidates even if they were represented or coded differently. We show dramatic improvements on multiple AutoML domains, including neural architecture search and algorithm discovery.
  arXiv  Detail & Related papers  (2023-02-10T18:50:37Z)
• Keyword Embeddings for Query Suggestion [3.7900158137749322]
  This paper proposes two novel models for the keyword suggestion task trained on scientific literature. Our techniques adapt the architecture of Word2Vec and FastText to generate keyword embeddings by leveraging documents' keyword co-occurrence. We evaluate our proposals against the state-of-the-art word and sentence embedding models showing considerable improvements over the baselines for the tasks.
  arXiv  Detail & Related papers  (2023-01-19T11:13:04Z)
• On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting [51.41426141283203]
  User-defined keyword spotting is a task to detect new spoken terms defined by users. Previous works try to incorporate self-supervised learning models or apply meta-learning algorithms. Our result shows that HuBERT combined with Matching network achieves the best result.
  arXiv  Detail & Related papers  (2022-04-01T10:59:39Z)
• Algorithm Selection on a Meta Level [58.720142291102135]
  We introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework.
  arXiv  Detail & Related papers  (2021-07-20T11:23:21Z)
• Towards Meta-Algorithm Selection [78.13985819417974]
  Instance-specific algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidates. We show that meta-algorithm-selection can indeed prove beneficial in some cases.
  arXiv  Detail & Related papers  (2020-11-17T17:27:33Z)
• Class LM and word mapping for contextual biasing in End-to-End ASR [4.989480853499918]
  In recent years, all-neural, end-to-end (E2E) ASR systems gained rapid interest in the speech recognition community. In this paper, we propose an algorithm to train a context aware E2E model and allow the beam search to traverse into the context FST during inference. Although an E2E model does not need pronunciation dictionary, it's interesting to make use of existing pronunciation knowledge to improve accuracy.
  arXiv  Detail & Related papers  (2020-07-10T20:58:44Z)
• Research on Annotation Rules and Recognition Algorithm Based on Phrase Window [4.334276223622026]
  We propose labeling rules based on phrase windows, and designed corresponding phrase recognition algorithms. The labeling rule uses phrases as the minimum unit, di-vides sentences into 7 types of nestable phrase types, and marks the grammatical dependencies between phrases. The corresponding algorithm, drawing on the idea of identifying the target area in the image field, can find the start and end positions of various phrases in the sentence.
  arXiv  Detail & Related papers  (2020-07-07T00:19:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.