How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
SoundingActions: Learning How Actions Sound from Narrated Egocentric Videos
We propose a novel self-supervised embedding to learn how actions sound from narrated in-the-wild egocentric videos. Whereas existing methods rely on curated data with known audio-visual correspondence, our multimodal co…
A Signal Subspace Rotation Method for Localization of Multiple Wideband Sound Sources
In this paper, the problem of extending narrowband multichannel sound source localization algorithms to the wideband case is addressed. The DOA estimation of narrowband algorithms is based on the estimate of inter-channe…
Sound Source LocalizationDG-PPU: Dynamical Graphs based Post-processing of Point Clouds extracted from Knee Ultrasounds
Patients undergoing total knee arthroplasty (TKA) often experience non-specific anterior knee pain, arising from abnormal patellofemoral joint (PFJ) instability. Tracking PFJ motion is challenging since static imaging mo…
AnatomyUnsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma
This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of unsupervised-ADS is to detect unknown anomalous sound wit…
Anomaly DetectionLEMMAUnsupervised Anomaly DetectionUnsupervised Anomaly Detection In SoundGlobal Multi-modal 2D/3D Registration via Local Descriptors Learning
Multi-modal registration is a required step for many image-guided procedures, especially ultrasound-guided interventions that require anatomical context. While a number of such registration algorithms are already availab…