Takashi Nose

1.6k total citations
116 papers, 1.1k citations indexed

About

Takashi Nose is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Takashi Nose has authored 116 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 99 papers in Artificial Intelligence, 69 papers in Signal Processing and 22 papers in Computer Vision and Pattern Recognition. Recurrent topics in Takashi Nose's work include Speech Recognition and Synthesis (85 papers), Speech and Audio Processing (67 papers) and Speech and dialogue systems (40 papers). Takashi Nose is often cited by papers focused on Speech Recognition and Synthesis (85 papers), Speech and Audio Processing (67 papers) and Speech and dialogue systems (40 papers). Takashi Nose collaborates with scholars based in Japan, France and United States. Takashi Nose's co-authors include Takao Kobayashi, Junichi Yamagishi, Takashi Masuko, Heiga Zen, Keiichi Tokuda, Akinori Ito, Shinji Sako, Alan W. Black, Tomoki Koriyama and Tomoki Toda and has published in prestigious journals such as The Journal of the Acoustical Society of America, IEEE Journal of Selected Topics in Signal Processing and IEEE Transactions on Audio Speech and Language Processing.

In The Last Decade

Takashi Nose

103 papers receiving 954 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Takashi Nose Japan 14 962 697 222 93 42 116 1.1k
Seiichi Nakagawa Japan 21 1.3k 1.4× 1.0k 1.5× 159 0.7× 187 2.0× 39 0.9× 257 1.6k
Sanyuan Chen China 13 1.1k 1.1× 763 1.1× 136 0.6× 126 1.4× 12 0.3× 26 1.4k
Zdravko Kačič Slovenia 16 511 0.5× 384 0.6× 129 0.6× 106 1.1× 36 0.9× 79 720
Zhengyang Chen China 14 1.1k 1.2× 892 1.3× 137 0.6× 145 1.6× 13 0.3× 50 1.4k
Berrak Şişman Singapore 19 926 1.0× 726 1.0× 150 0.7× 150 1.6× 26 0.6× 59 1.2k
Rohit Sinha India 19 946 1.0× 900 1.3× 112 0.5× 130 1.4× 14 0.3× 123 1.2k
Jonathan Shen United States 5 1.4k 1.5× 936 1.3× 90 0.4× 205 2.2× 24 0.6× 7 1.6k
Sachin Kajarekar United States 20 1.3k 1.3× 1.2k 1.7× 84 0.4× 119 1.3× 44 1.0× 52 1.4k
Petr Motlíček Switzerland 21 1.1k 1.2× 831 1.2× 98 0.4× 114 1.2× 69 1.6× 155 1.4k
Pegah Ghahremani United States 12 1.1k 1.2× 901 1.3× 136 0.6× 65 0.7× 9 0.2× 18 1.3k

Countries citing papers authored by Takashi Nose

Since Specialization
Citations

This map shows the geographic impact of Takashi Nose's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Takashi Nose with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takashi Nose more than expected).

Fields of papers citing papers by Takashi Nose

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Takashi Nose. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Takashi Nose. The network helps show where Takashi Nose may publish in the future.

Co-authorship network of co-authors of Takashi Nose

This figure shows the co-authorship network connecting the top 25 collaborators of Takashi Nose. A scholar is included among the top collaborators of Takashi Nose based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Takashi Nose. Takashi Nose is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Nose, Takashi, et al.. (2016). Construction and analysis of phonetically and prosodically balanced emotional speech database. 16–21. 22 indexed citations
2.
Nose, Takashi, et al.. (2015). Entropy-based sentence selection for speech synthesis using phonetic and prosodic contexts. 3491–3495. 2 indexed citations
4.
Nose, Takashi, et al.. (2012). A speech parameter generation algorithm using local variance for HMM-based speech synthesis. Conference of the International Speech Communication Association. 1151–1154. 2 indexed citations
5.
Nose, Takashi & Takao Kobayashi. (2011). Recent development of HMM-based expressive speech synthesis and its applications. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 11 indexed citations
6.
Nose, Takashi, et al.. (2011). A tone-modeling technique using a quantized F0 context to improve tone correctness in average-voice-based speech synthesis. Speech Communication. 54(2). 245–255. 4 indexed citations
7.
Nose, Takashi & Takao Kobayashi. (2011). Very low bit-rate F0 coding for phonetic vocoder using MSD-HMM with quantized F0 context. 3. 5236–5239. 2 indexed citations
8.
Nose, Takashi & Takao Kobayashi. (2010). HMM-based robust voice conversion using adaptive F0 quantization.. SSW. 80–85. 1 indexed citations
9.
Nakano, Mikio, Naoto Iwahashi, Takayuki Nagai, et al.. (2010). Grounding New Words on the Physical World in Multi-Domain Human-Robot Dialogues. National Conference on Artificial Intelligence. 74–79. 5 indexed citations
10.
Zen, Heiga, Keiichiro Oura, Takashi Nose, et al.. (2009). Recent development of the HMM-based speech synthesis system (HTS). Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University). 121–130. 24 indexed citations
11.
Taguchi, Ryo, Naoto Iwahashi, Takashi Nose, Kotaro Funakoshi, & Mikio Nakano. (2009). Learning lexicons from spoken utterances based on statistical model selection. 2731–2734. 7 indexed citations
12.
Tachibana, Makoto, et al.. (2008). An MRHSMM-based voice quality control technique for synthetic speech using speaker adaptation from average voice model. IEICE technical report. Speech. 108(265). 41–46.
13.
Nose, Takashi, et al.. (2008). 72.4: Novel LCD Driver ICs with Built‐In Programmable RGB Independent Gamma Control. SID Symposium Digest of Technical Papers. 39(1). 1123–1126. 3 indexed citations
14.
Nose, Takashi, et al.. (2007). 56.1: Invited Paper : A 12‐bit LCD Source Driver IC with Point‐to‐Point Link Interface. SID Symposium Digest of Technical Papers. 38(1). 1633–1635. 19 indexed citations
15.
Zen, Heiga, Takashi Nose, Junichi Yamagishi, et al.. (2007). The HMM-based speech synthesis system (HTS) version 2.0.. SSW. 294–299. 259 indexed citations
16.
Nose, Takashi, Junichi Yamagishi, Takashi Masuko, & Takao Kobayashi. (2007). A Style Control Technique for HMM-Based Expressive Speech Synthesis(Speech and Hearing). IEICE Transactions on Information and Systems. 90(9). 1406–1413. 4 indexed citations
17.
Satō, Kazuo, et al.. (2002). Sound analysis of a small scale-polishing machine for rice of sake-making. JOURNAL OF THE BREWING SOCIETY OF JAPAN. 97(12). 872–877. 1 indexed citations
18.
Nose, Takashi, et al.. (2001). 35. 3: A Black-Stripe Driving Scheme of Displaying Motion Pictures on LCDs(Session35: Video Quality AMLCDs II)(Report on 2001 SID). 25(46). 21. 1 indexed citations
19.
Nose, Takashi, et al.. (1999). LCD Legibility as a Function of Resolution. IEICE Transactions on Electronics. 82(10). 1792–1797. 5 indexed citations
20.
Nose, Takashi, et al.. (1993). Automatic Generation of Expressive Performance by using Music Structures.. The Journal of the Abraham Lincoln Association. 1993. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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