1. Introduction

A pronunciation engine is a fundamental component of any text-to-speech system. In languages with limited computational resources, rule-based methods are often more practical than data-driven models.

A rule-based converter transforms written text into phonetic representation using predefined linguistic rules.

Bishnupriya Manipuri Script
            ↓
Grapheme Analysis
            ↓
Phonological Rules
            ↓
IPA Output

This method ensures predictable pronunciation and works well for dictionary applications and speech synthesis systems.

2. Unicode Normalization

The first step in conversion is normalizing the text. Bishnupriya Manipuri uses Unicode characters from the Eastern Nagari block. Some text sources may contain inconsistent encoding or composed characters.

Normalization ensures that the input text follows a consistent Unicode form before processing begins.

Input text → Unicode normalization → standard character sequence

This step prevents errors in later phonetic conversion stages.

3. Grapheme Parsing

After normalization, the text is divided into graphemes. A grapheme represents a meaningful written unit, usually a consonant combined with a vowel sign.

The grapheme structure becomes:

অ + ক্ষ + র

Each grapheme is then converted into phonetic units using mapping rules.

4. Consonant Mapping Rules

Each consonant letter corresponds to a specific IPA symbol.

5. Vowel Mapping Rules

Independent vowels and vowel signs map directly to IPA vowels.

6. Handling Consonant Clusters

Many Bishnupriya Manipuri words contain consonant clusters, often inherited from Sanskrit.

Grapheme sequence:

অ + গ্নি

Phonetic result:

ɔgni

The converter must detect cluster markers such as the virama (্) and combine consonants correctly.

7. Schwa Rules

In Eastern Nagari scripts, consonants normally contain an inherent vowel. However, this vowel is often deleted in certain positions.

Rule-based schwa deletion ensures natural pronunciation.

8. IPA Generation Process

The complete rule-based pipeline works as follows:

Input Word
     ↓
Unicode normalization
     ↓
Grapheme parsing
     ↓
Consonant and vowel mapping
     ↓
Schwa rules
     ↓
Cluster handling
     ↓
IPA output

9. Advantages of a Rule-Based Converter

• Predictable pronunciation output
• Works well for dictionary systems
• Easy to modify linguistic rules
• Does not require large training data
• Suitable for low-resource languages

10. Conclusion

A rule-based IPA converter provides a reliable foundation for Bishnupriya Manipuri speech technology.

Once IPA transcription is generated, the output can be used for phoneme extraction, diphone segmentation, and speech synthesis.

Script → IPA → Phoneme → Diphone → TTS

Future improvements may incorporate statistical pronunciation models or neural speech synthesis techniques.