--- id: ishihara26b_interspeech title: Band-Limited Cepstral Analysis of Speaker Sensitivity in Forensic Voice Comparison authors: - Shunichi Ishihara - Satoru Tsuge - Frantz Clermont year: 2026 doi: 10.21437/Interspeech.2026-1028 isca_url: https://www.isca-archive.org/interspeech_2026/ishihara26b_interspeech.html pdf_url: https://www.isca-archive.org/interspeech_2026/ishihara26b_interspeech.pdf session: Speaker-Specific, Forensic and Segmental Characteristics of Typical and Atypical Speech topics: - speaker-verification - forensic-voice-comparison - evaluation category: speaker institutions: - Australian National University - Daido University code: url: "" license: "" open_to_collaboration: false wiki_frontmatter: id: ishihara26b_interspeech category: speaker institutions: - Australian National University - Daido University updated: 2026-09-29 confidence: full-paper digest: v2 source: https://doi.org/10.21437/Interspeech.2026-1028 pdf: https://www.isca-archive.org/interspeech_2026/ishihara26b_interspeech.pdf wiki_url: https://interspeech-2026-wiki.vercel.app/papers/ishihara26b_interspeech/ markdown_url: https://interspeech-2026-wiki.vercel.app/papers/ishihara26b_interspeech/markdown.md --- # Band-Limited Cepstral Analysis of Speaker Sensitivity in Forensic Voice Comparison *Shunichi Ishihara, Satoru Tsuge, Frantz Clermont* [PDF](https://www.isca-archive.org/interspeech_2026/ishihara26b_interspeech.pdf) · [ISCA page](https://www.isca-archive.org/interspeech_2026/ishihara26b_interspeech.html) · [DOI](https://doi.org/10.21437/Interspeech.2026-1028) **Category:** `speaker` **TL;DR** — This paper investigates the distribution of speaker-discriminative information across different frequency sub-bands using Band-Limited Cepstral Coefficients (BLCCs) for forensic voice comparison, finding that the 0–0.6 kHz and 4–6 kHz regions provide the strongest speaker cues while frequencies above 7 kHz are the least informative. ## Key contributions - Evaluated the speaker-discriminative information of individual sub-bands across the 0–8 kHz full-band spectrum using systematic 0.6 kHz and 1.2 kHz BLCC scans. - Demonstrated that while all sub-bands yield useful speaker-specific information (Cllr < 1), speaker sensitivity is highly non-uniform, peaking in the 0–0.6 kHz and 4–6 kHz ranges. - Showed that fusing likelihood ratios from strategically selected, non-overlapping sub-bands significantly improves forensic voice comparison performance compared to individual sub-bands. - Established a flexible linear transformation pipeline for extracting sub-band cepstral coefficients without needing to re-analyze the original acoustic signals. ## Problem Prior forensic voice comparison studies disagree on precisely which frequency bands carry the most speaker-specific information, with existing works pointing variously to low, mid, or mid-to-high frequency regions. Furthermore, conventional sub-band analyses require computationally expensive re-processing of the original speech signal for every frequency range of interest. Understanding these spectral distributions is crucial for providing transparent, interpretable evidence in forensic contexts. ## Method The study employs Band-Limited Cepstral Coefficients (BLCCs) derived via a three-step pipeline: short-time filter-bank spectral analysis with a linear frequency scale (25 triangular filters, 25-ms frames, 5-ms shift), extraction of 14 full-band linear-frequency cepstral coefficients (LFCCs), and a linear transformation matrix converting full-band cepstra to sub-band cepstra. The 0–8 kHz frequency range was systematically scanned using 0.6 kHz sub-bands (shifted by 0.2 kHz, yielding 2 BLCC coefficients) and 1.2 kHz sub-bands (shifted by 0.4 kHz, yielding 3 BLCC coefficients). Experiments were conducted within a Gaussian Mixture Model–Universal Background Model (GMM-UBM) framework utilizing diagonal-covariance models with 256 Gaussian components and mean-only MAP adaptation. System scores were converted to likelihood ratios using logistic-regression calibration across a 6-fold rotation scheme. ## Experimental setup Evaluated on pseudo-police interview recordings from the AusEng 500+ database comprising 30 seconds of running speech per session from 162 native male Australian English speakers. Systems were compared against a full-band LFCC baseline (Cllr = 0.13643, Ccal_llr = 0.02894, EER = 3.396%). Evaluation metrics include the log-likelihood-ratio cost (Cllr), its calibration component (Ccal_llr), and equal error rate (EER). ## Results All individual sub-bands achieved Cllr values below 1.0, but performance varied sharply across the spectrum. For 0.6 kHz sub-bands, the lowest Cllr (strongest discrimination) occurred at the 0–0.6 kHz band (0.58339) and a secondary prominent dip around 4–5 kHz, whereas frequencies above 7 kHz showed the weakest performance, with the final sub-band reaching Cllr values up to 0.82571. Fusing the likelihood ratios of up to four well-spaced, highly informative sub-bands substantially outperformed any individual sub-band and improved upon the full-band LFCC baseline. | System / Condition | Cllr | C_cal,llr | EER (%) | |---|---|---|---| | Full-Band LFCC Baseline | 0.13643 | 0.02894 | 3.396 | | Best 0.6 kHz Sub-band (0–0.6 kHz) | 0.58339 | — | — | | Worst 0.6 kHz Sub-band (Final band) | 0.82571 | — | — | | Best 1.2 kHz Sub-band | ~0.50 | — | — | | Worst 1.2 kHz Sub-band (Final band) | 0.67020 | — | — | ## Limitations The study is restricted to 162 native male speakers of a single accent (Australian English) using clean pseudo-interview speech sampled at 16 kHz, limiting generalizability to female speakers, cross-linguistic scenarios, and telephony channels. Sub-band combinations were constrained to a maximum of four sub-bands due to computational limitations in the fusion analysis. ## Why read this Speech and forensic audio researchers will find this paper valuable for its rigorous mapping of speaker-discriminative cues across frequency bands using BLCCs, offering a blueprint for building multi-band or weighted feature-fusion systems. ## Code None released (as of this page's `updated` date). If you are an author with a repo, please claim this entry — see CONTRIBUTING.md. ## Applications Forensic voice comparison, speaker verification, and multi-band acoustic feature weighting for speaker recognition. ## Institutions / 機構 Australian National University, Daido University ## Related - [Sub-band Cepstral Analysis of Speaker-Specific Information: A Case Study of Japanese Word /saN/](ishihara26_interspeech.md) — shared technique · relatedness 3.0/3 - [Function words: a topic independent approach to word n-gram selection for forensic speaker comparison](carne26_interspeech.md) — same problem · relatedness 1.7/3 - [Codec-induced Mismatch, Speech Duration, and Speaker-dependent Effect in a DNN-based Forensic Speaker Recognition System](deng26b_interspeech.md) — same problem · relatedness 1.6/3 - [Human-like cross-language generalisation in deep neural speaker embeddings and its acoustic foundations](xu26f_interspeech.md) — same problem · relatedness 1.6/3 - [Do speech foundation models perceive speaker similarity as humans do?](kishi26_interspeech.md) — same problem · relatedness 1.6/3 All 950k paper pairs scored by TypeSafe Jev (`scripts/related/`); relatedness 0 = unrelated … 3 = directly comparable.