diff --git a/src/phoneRecognition.cpp b/src/phoneRecognition.cpp
index b2c5e88..6931c76 100644
--- a/src/phoneRecognition.cpp
+++ b/src/phoneRecognition.cpp
@@ -45,6 +45,7 @@ using std::chrono::duration;
 using boost::optional;
 using std::u32string;
 using std::chrono::duration_cast;
+using std::array;
 
 constexpr int sphinxSampleRate = 16000;
 
@@ -237,6 +238,48 @@ void addMissingDictionaryWords(const vector<string>& words, ps_decoder_t& decode
 	}
 }
 
+lambda_unique_ptr<ngram_model_t> createDefaultLanguageModel(ps_decoder_t& decoder) {
+	path modelPath = getSphinxModelDirectory() / "en-us.lm.bin";
+	lambda_unique_ptr<ngram_model_t> result(
+		ngram_model_read(decoder.config, modelPath.string().c_str(), NGRAM_AUTO, decoder.lmath),
+		[](ngram_model_t* lm) { ngram_model_free(lm); });
+	if (!result) {
+		throw runtime_error(fmt::format("Error reading language model from {}.", modelPath));
+	}
+
+	return std::move(result);
+}
+
+lambda_unique_ptr<ngram_model_t> createDialogLanguageModel(ps_decoder_t& decoder, const u32string& dialog) {
+	// Split dialog into normalized words
+	vector<string> words = tokenizeText(dialog, [&](const string& word) { return dictionaryContains(*decoder.dict, word); });
+
+	// Add dialog-specific words to the dictionary
+	addMissingDictionaryWords(words, decoder);
+
+	// Create dialog-specific language model
+	words.insert(words.begin(), "<s>");
+	words.push_back("</s>");
+	return createLanguageModel(words, decoder);
+}
+
+lambda_unique_ptr<ngram_model_t> createBiasedLanguageModel(ps_decoder_t& decoder, const u32string& dialog) {
+	auto defaultLanguageModel = createDefaultLanguageModel(decoder);
+	auto dialogLanguageModel = createDialogLanguageModel(decoder, dialog);
+	constexpr int modelCount = 2;
+	array<ngram_model_t*, modelCount> languageModels{ defaultLanguageModel.get(), dialogLanguageModel.get() };
+	array<char*, modelCount> modelNames{ "defaultLM", "dialogLM" };
+	array<float, modelCount> modelWeights{ 0.1f, 0.9f };
+	lambda_unique_ptr<ngram_model_t> result(
+		ngram_model_set_init(nullptr, languageModels.data(), modelNames.data(), modelWeights.data(), modelCount),
+		[](ngram_model_t* lm) { ngram_model_free(lm); });
+	if (!result) {
+		throw runtime_error("Error creating biased language model.");
+	}
+
+	return std::move(result);
+}
+
 lambda_unique_ptr<ps_decoder_t> createDecoder(optional<u32string> dialog) {
 	lambda_unique_ptr<cmd_ln_t> config(
 		cmd_ln_init(
@@ -261,22 +304,9 @@ lambda_unique_ptr<ps_decoder_t> createDecoder(optional<u32string> dialog) {
 	if (!decoder) throw runtime_error("Error creating speech decoder.");
 
 	// Set language model
-	lambda_unique_ptr<ngram_model_t> languageModel;
-	if (dialog) {
-		// Create dialog-specific language model
-		vector<string> words = tokenizeText(*dialog, [&](const string& word) { return dictionaryContains(*decoder->dict, word); });
-		words.insert(words.begin(), "<s>");
-		words.push_back("</s>");
-		languageModel = createLanguageModel(words, *decoder);
-
-		// Add any dialog-specific words to the dictionary
-		addMissingDictionaryWords(words, *decoder);
-	} else {
-		path modelPath = getSphinxModelDirectory() / "en-us.lm.bin";
-		languageModel = lambda_unique_ptr<ngram_model_t>(
-			ngram_model_read(decoder->config, modelPath.string().c_str(), NGRAM_AUTO, decoder->lmath),
-			[](ngram_model_t* lm) { ngram_model_free(lm); });
-	}
+	lambda_unique_ptr<ngram_model_t> languageModel(dialog
+		? createBiasedLanguageModel(*decoder, *dialog)
+		: createDefaultLanguageModel(*decoder));
 	ps_set_lm(decoder.get(), "lm", languageModel.get());
 	ps_set_search(decoder.get(), "lm");