Masami Akamine

543 total citations
45 papers, 387 citations indexed

About

Masami Akamine is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Masami Akamine has authored 45 papers receiving a total of 387 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Signal Processing, 36 papers in Artificial Intelligence and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Masami Akamine's work include Speech Recognition and Synthesis (35 papers), Speech and Audio Processing (32 papers) and Music and Audio Processing (19 papers). Masami Akamine is often cited by papers focused on Speech Recognition and Synthesis (35 papers), Speech and Audio Processing (32 papers) and Music and Audio Processing (19 papers). Masami Akamine collaborates with scholars based in Japan, South Korea and United Kingdom. Masami Akamine's co-authors include Javier Latorre, Mark Gales, Ranniery Maia, Yannis Stylianou, Thomas Drugman, Vincent Wan, Yamato Ohtani, Kate Knill, Jitendra Ajmera and K. K. Chin and has published in prestigious journals such as IEEE Signal Processing Letters, IEEE Journal of Selected Topics in Signal Processing and Speech Communication.

In The Last Decade

Masami Akamine

43 papers receiving 329 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Masami Akamine Japan 11 321 297 52 45 15 45 387
Thomas Pellegrini France 11 240 0.7× 159 0.5× 54 1.0× 58 1.3× 21 1.4× 49 370
Sankaran Panchapagesan United States 9 322 1.0× 263 0.9× 38 0.7× 27 0.6× 8 0.5× 17 385
Harald Höge Germany 11 362 1.1× 280 0.9× 49 0.9× 51 1.1× 7 0.5× 52 428
Wooil Kim United States 11 161 0.5× 213 0.7× 38 0.7× 50 1.1× 28 1.9× 42 287
Christian Fuegen United States 12 691 2.2× 427 1.4× 54 1.0× 39 0.9× 7 0.5× 33 789
Charles Jankowski United States 7 218 0.7× 245 0.8× 37 0.7× 27 0.6× 15 1.0× 11 282
Akira Kurematsu Japan 7 212 0.7× 240 0.8× 114 2.2× 23 0.5× 42 2.8× 36 382
Aruna Bayya United States 6 262 0.8× 286 1.0× 48 0.9× 34 0.8× 29 1.9× 11 336
Murat Akbacak United States 12 349 1.1× 311 1.0× 90 1.7× 30 0.7× 12 0.8× 32 469
Yanhua Long China 9 226 0.7× 197 0.7× 23 0.4× 18 0.4× 13 0.9× 62 280

Countries citing papers authored by Masami Akamine

Since Specialization
Citations

This map shows the geographic impact of Masami Akamine'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 Masami Akamine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masami Akamine more than expected).

Fields of papers citing papers by Masami Akamine

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Masami Akamine. 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 Masami Akamine. The network helps show where Masami Akamine may publish in the future.

Co-authorship network of co-authors of Masami Akamine

This figure shows the co-authorship network connecting the top 25 collaborators of Masami Akamine. A scholar is included among the top collaborators of Masami Akamine 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 Masami Akamine. Masami Akamine 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.
Kobayashi, Yuka, Takami Yoshida, Kenji Iwata, Hiroshi Fujimura, & Masami Akamine. (2018). Out-of-Domain Slot Value Detection for Spoken Dialogue Systems with Context Information. 15. 854–861. 2 indexed citations
2.
Ohtani, Yamato, et al.. (2016). Statistical Bandwidth Extension for Speech Synthesis Based on Gaussian Mixture Model with Sub-Band Basis Spectrum Model. IEICE Transactions on Information and Systems. E99.D(10). 2481–2489.
3.
Drugman, Thomas, et al.. (2015). Voice Activity Detection: Merging Source and Filter-based Information. IEEE Signal Processing Letters. 23(2). 252–256. 59 indexed citations
4.
Maia, Ranniery, Yannis Stylianou, & Masami Akamine. (2015). A maximum likelihood approach to the detection of moments of maximum excitation and its application to high-quality speech parameterization. 603–607. 1 indexed citations
5.
Wan, Vincent, et al.. (2014). Building HMM-TTS Voices on Diverse Data. IEEE Journal of Selected Topics in Signal Processing. 8(2). 296–306. 6 indexed citations
6.
Ohtani, Yamato, et al.. (2014). GMM-based bandwidth extension using sub-band basis spectrum model. 2489–2493. 13 indexed citations
7.
Wan, Vincent, Robert Anderson, Norbert Braunschweiler, et al.. (2013). Photo-realistic expressive text to talking head synthesis. Cambridge University Engineering Department Publications Database. 2667–2669. 10 indexed citations
8.
Maia, Ranniery, Mark Gales, Yannis Stylianou, & Masami Akamine. (2013). Minimum mean squared error based warped complex cepstrum analysis for statistical parametric speech synthesis. 2336–2340. 3 indexed citations
9.
Maia, Ranniery, Masami Akamine, & Mark Gales. (2013). Complex cepstrum analysis based on the minimum mean squared error. 7972–7976. 2 indexed citations
10.
Maia, Ranniery, Masami Akamine, & Mark Gales. (2012). Complex cepstrum as phase information in statistical parametric speech synthesis. 26. 4581–4584. 24 indexed citations
11.
Latorre, Javier, Vincent Wan, Mark Gales, et al.. (2012). Speech factorization for HMM-TTS based on cluster adaptive training. 971–974. 21 indexed citations
12.
Gales, Mark, et al.. (2012). Exploring rich expressive information from audiobook data using cluster adaptive training. 959–962. 26 indexed citations
13.
Latorre, Javier, Sabine Buchholz, & Masami Akamine. (2010). Usages of an external duration model for HMM-based speech synthesis.. paper 073–0. 5 indexed citations
14.
Braunschweiler, Norbert, et al.. (2010). Unit selection speech synthesis using multiple speech units at non-adjacent segments for prosody and waveform generation. e88 d. 4802–4805. 2 indexed citations
15.
Yamamoto, Kōichi, et al.. (2008). Comparative evaluation of different methods for voice activity detection. 107–110. 1 indexed citations
16.
Ajmera, Jitendra & Masami Akamine. (2008). Speech recognition using soft decision trees. 940–943. 2 indexed citations
17.
Akamine, Masami, et al.. (2007). HMM-based speech recognition using decision trees instead of GMMs. 2097–2100. 4 indexed citations
18.
Akamine, Masami, et al.. (1998). Automatic Generation of Synthesis Units by Units Selection Based on Closed Loop Training. Transactions of the Institute of Electronics, Information and Communication Engineers. 81(9). 1949–1954. 1 indexed citations
19.
20.
Akamine, Masami, et al.. (1995). Adaptive density pulse excitation for low bit rate speech coding. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 78(2). 199–207. 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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