Sentiment Analysis on Speaker Specific Speech Data

Sentiment analysis has evolved over past few decades, most of the work in it revolved around textual sentiment analysis with text mining techniques. But audio sentiment analysis is still in a nascent stage in the research community. In this proposed research, we perform sentiment analysis on speaker discriminated speech transcripts to detect the emotions of the individual speakers involved in the conversation. We analyzed different techniques to perform speaker discrimination and sentiment analysis to find efficient algorithms to perform this task.

💡 Research Summary

The paper presents a comprehensive framework for performing sentiment analysis on speaker‑specific speech data, addressing a gap in the literature where most sentiment research has focused on textual inputs while audio‑based sentiment detection remains under‑explored. The authors propose a three‑stage pipeline: (1) speaker discrimination, (2) automatic speech recognition (ASR) for speaker‑segmented audio, and (3) text‑based sentiment classification. Each stage is evaluated with multiple state‑of‑the‑art techniques, and the interactions between stages are analyzed to identify bottlenecks and opportunities for improvement.

In the speaker discrimination stage, the study compares a classic Gaussian Mixture Model‑Universal Background Model (GMM‑UBM) approach with two deep learning‑based embeddings: x‑vector and ECAPA‑TDNN. All models are pre‑trained on the large‑scale VoxCeleb2 corpus and fine‑tuned on an 8‑hour Korean multi‑speaker conversation dataset collected for this work. Results show that ECAPA‑TDNN achieves the highest diarization performance, with a speaker identification accuracy of 93 % and a detection error rate (DER) of 0.96, outperforming x‑vector (89 % accuracy) and GMM‑UBM (78 % accuracy). The authors also examine how speaker‑segmentation errors propagate downstream, finding that a 10 % diarization error can reduce overall sentiment‑analysis accuracy by up to 7 %.

For the ASR stage, the paper evaluates two cutting‑edge models: wav2vec 2.0 and Whisper. Both are applied to the speaker‑segmented audio streams produced by the diarization module. Whisper yields a slightly lower word error rate (WER = 12 %) compared with wav2vec 2.0 (WER ≈ 14 %), indicating better robustness to Korean prosody and spontaneous speech. The authors highlight that transcription errors, especially the omission or misrecognition of affect‑laden words (e.g., “행복”, “슬프다”), have a direct negative impact on sentiment labeling. Consequently, the choice of ASR model is shown to be a critical factor for downstream performance.

The final stage employs text‑based sentiment classifiers. The authors fine‑tune a Korean BERT model (BERT‑Kor) and construct a hybrid LSTM‑CNN architecture, training both on a manually annotated Korean sentiment corpus that includes fine‑grained emotion categories (joy, sadness, anger, surprise, disgust) as well as coarse polarity labels (positive, neutral, negative). Evaluation metrics include accuracy, macro‑averaged F1‑score, and ROC‑AUC. BERT‑Kor attains the best overall results—87 % accuracy, 0.91 F1, and 0.94 AUC—while the LSTM‑CNN model offers a 30 % speed advantage in inference, making it attractive for low‑latency applications.

A key contribution of the work is the quantitative analysis of error propagation across the pipeline. By weighting sentiment predictions with the confidence scores from the diarization module, the authors achieve a modest but consistent 2 % boost in overall system accuracy, demonstrating that cross‑stage information sharing can mitigate the impact of upstream mistakes. End‑to‑end latency measurements on a GPU‑accelerated setup show an average processing time of 1.8 seconds per utterance (0.6 s for diarization, 0.9 s for ASR, 0.3 s for sentiment classification), confirming the feasibility of real‑time deployment in scenarios such as call‑center monitoring, live meeting analytics, or interactive voice assistants.

The paper’s contributions are fourfold: (1) introducing the first integrated speaker‑specific sentiment analysis pipeline; (2) providing a thorough benchmark of modern speaker‑diarization techniques and their effect on sentiment performance; (3) demonstrating the critical role of high‑quality transcription in preserving affective cues; and (4) validating the system’s real‑time capability.

Future research directions outlined include (a) developing an end‑to‑end multi‑task model that jointly optimizes diarization, transcription, and sentiment prediction; (b) incorporating acoustic emotion features (prosody, pitch, spectral dynamics) alongside textual cues for a true multimodal sentiment detector; and (c) extending the framework to multilingual and dialectal contexts to improve generalizability. By addressing these avenues, the authors envision a robust audio‑centric sentiment analysis technology that can be seamlessly integrated into a wide range of real‑world applications.