diff --git a/gentle/generate_wp.py b/gentle/generate_wp.py
deleted file mode 100644
index 11fb51f0..00000000
--- a/gentle/generate_wp.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Take a transcript, and generate a textual FST containing a bigram language model
-
-import sys
-import re
-import math
-
-def generate_word_sequence(words_file, transcript_file):
-    # XXX: If we refactored as a class, we could avoid repeatedly
-    # loading the vocabulary
-    vocabulary = set([X.split(' ')[0] for X in open(words_file).read().split('\n')])
-
-    # Despite ARPA being all-caps, we may need lowercase.
-    transcript = open(transcript_file).read().lower()
-
-    # Turn hyphens into spaces
-    transcript = transcript.replace('-', ' ')
-
-    # Get rid of all punctuation except for periods and spaces
-    # XXX: getting rid of periods
-    transcript = re.sub(r'[^a-z\s\']', '', transcript)
-
-    # # Replace periods with the SENTENCE-END sentinel
-    # transcript = transcript.replace(".", " . ")
-
-    word_sequence = [X.strip() for X in transcript.split() if len(X.strip()) > 0 and not X.startswith("'")]
-
-    # We need to limit to words within a vocabulary
-    word_sequence = [X if X in vocabulary else '[oov]' for X in word_sequence]
-    return word_sequence
-
-def language_model_from_word_sequence(word_sequence):
-    word_sequence = ['[oov]', '[oov]'] + word_sequence + ['[oov]']
-
-    bigrams = {}
-    prev_word = word_sequence[0]
-    for word in word_sequence[1:]:
-        bigrams.setdefault(prev_word, set()).add(word)
-        prev_word = word
-
-    node_ids = {}
-    def get_node_id(word):
-        node_id = node_ids.get(word, len(node_ids) + 1)
-        node_ids[word] = node_id
-        return node_id
-
-    output = ""
-    for from_word in sorted(bigrams.keys()):
-        from_id = get_node_id(from_word)
-
-        successors = bigrams[from_word]
-        if len(successors) > 0:
-            weight = -math.log(1.0 / len(successors))
-        else:
-            weight = 0
-
-        for to_word in sorted(successors):
-            to_id = get_node_id(to_word)
-            output += '%d    %d    %s    %s    %f' % (from_id, to_id, to_word, to_word, weight)
-            output += "\n"
-
-    output += "%d    0\n" % (len(node_ids))
-
-    return output
-
-
-if __name__=='__main__':
-
-    USAGE = "python generate_wp.py WORDS.TXT TRANSCRIPT OUTPUT_FILE"
-    if len(sys.argv) != 4:
-        print USAGE
-        sys.exit(1)
-
-    WORDS_FILE = sys.argv[1]
-    TRANSCRIPT_FILE = sys.argv[2]
-    OUTPUT_FILE = sys.argv[3]
-
-    word_sequence = generate_word_sequence(WORDS_FILE, TRANSCRIPT_FILE)
-    lm = language_model_from_word_sequence(word_sequence)
-    open(OUTPUT_FILE, 'w').write(lm)
diff --git a/gentle/language_model.py b/gentle/language_model.py
index f0f8ef8d..8ea29bf0 100644
--- a/gentle/language_model.py
+++ b/gentle/language_model.py
@@ -1,52 +1,84 @@
 import logging
+import math
 import os
+import shutil
 import subprocess
 import sys
 import tempfile
 
 from paths import get_binary
-from generate_wp import language_model_from_word_sequence
+from metasentence import MetaSentence
 
 MKGRAPH_PATH = get_binary("mkgraph")
 
-def get_language_model(kaldi_seq, proto_langdir='PROTO_LANGDIR'):
-    """Generates a language model to fit the text
+def make_bigram_lm_fst(word_sequence):
+    '''
+    Use the given token sequence to make a bigram language model
+    in OpenFST plain text format.
+    '''
+    word_sequence = ['[oov]', '[oov]'] + word_sequence + ['[oov]']
+
+    bigrams = {}
+    prev_word = word_sequence[0]
+    for word in word_sequence[1:]:
+        bigrams.setdefault(prev_word, set()).add(word)
+        prev_word = word
+
+    node_ids = {}
+    def get_node_id(word):
+        node_id = node_ids.get(word, len(node_ids) + 1)
+        node_ids[word] = node_id
+        return node_id
+
+    output = ""
+    for from_word in sorted(bigrams.keys()):
+        from_id = get_node_id(from_word)
+
+        successors = bigrams[from_word]
+        if len(successors) > 0:
+            weight = -math.log(1.0 / len(successors))
+        else:
+            weight = 0
+
+        for to_word in sorted(successors):
+            to_id = get_node_id(to_word)
+            output += '%d    %d    %s    %s    %f' % (from_id, to_id, to_word, to_word, weight)
+            output += "\n"
+
+    output += "%d    0\n" % (len(node_ids))
+
+    return output
+
+def make_bigram_language_model(kaldi_seq, proto_langdir='PROTO_LANGDIR'):
+    """Generates a language model to fit the text.
+
+    Returns the filename of the generated language model FST.
+    The caller is resposible for removing the generated file.
 
     `proto_langdir` is a path to a directory containing prototype model data
     `kaldi_seq` is a list of words within kaldi's vocabulary.
     """
 
-    # Create a language model directory
-    lang_model_dir = tempfile.mkdtemp()
-    logging.info('saving language model to %s', lang_model_dir)
-
-    # Symlink in necessary files from the prototype directory
-    for dirpath, dirnames, filenames in os.walk(proto_langdir, followlinks=True):
-        for dirname in dirnames:
-            relpath = os.path.relpath(os.path.join(dirpath, dirname), proto_langdir)
-            os.makedirs(os.path.join(lang_model_dir, relpath))
-        for filename in filenames:
-            abspath = os.path.abspath(os.path.join(dirpath, filename))
-            relpath = os.path.relpath(os.path.join(dirpath, filename), proto_langdir)
-            dstpath = os.path.join(lang_model_dir, relpath)
-            os.symlink(abspath, dstpath)
-
     # Generate a textual FST
-    txt_fst = language_model_from_word_sequence(kaldi_seq)
-    txt_fst_file = os.path.join(lang_model_dir, 'G.txt')
-    open(txt_fst_file, 'w').write(txt_fst)
+    txt_fst = make_bigram_lm_fst(kaldi_seq)
+    txt_fst_file = tempfile.NamedTemporaryFile(delete=False)
+    txt_fst_file.write(txt_fst)
+    txt_fst_file.close()
     
-    words_file = os.path.join(proto_langdir, "graphdir/words.txt")
-    subprocess.check_output([MKGRAPH_PATH,
-                     os.path.join(lang_model_dir, 'langdir'),
-                     os.path.join(lang_model_dir, 'modeldir'),
-                     txt_fst_file,
-                     words_file,
-                     os.path.join(lang_model_dir, 'graphdir', 'HCLG.fst')])
+    hclg_filename = tempfile.mktemp(suffix='_HCLG.fst')
+    try:
+        subprocess.check_output([MKGRAPH_PATH,
+                        proto_langdir,
+                        txt_fst_file.name,
+                        hclg_filename])
+    except Exception, e:
+        os.unlink(hclg_filename)
+        raise e
+    finally:
+        os.unlink(txt_fst_file.name)
 
-    # Return the language model directory
-    return lang_model_dir
+    return hclg_filename
 
 if __name__=='__main__':
     import sys
-    get_language_model(open(sys.argv[1]).read())
+    make_bigram_language_model(open(sys.argv[1]).read())
diff --git a/gentle/language_model_transcribe.py b/gentle/language_model_transcribe.py
index 0b7720cc..a842bc14 100644
--- a/gentle/language_model_transcribe.py
+++ b/gentle/language_model_transcribe.py
@@ -27,18 +27,17 @@ def lm_transcribe(audio_f, transcript, proto_langdir, nnet_dir,
 
     ks = ms.get_kaldi_sequence()
 
-    gen_model_dir = language_model.get_language_model(ks, proto_langdir)
+    gen_hclg_filename = language_model.make_bigram_language_model(ks, proto_langdir)
+    try:
+        k = standard_kaldi.Kaldi(nnet_dir, gen_hclg_filename, proto_langdir)
 
-    gen_hclg_path = os.path.join(gen_model_dir, 'graphdir', 'HCLG.fst')
-    k = standard_kaldi.Kaldi(nnet_dir, gen_hclg_path, proto_langdir)
+        trans = standard_kaldi.transcribe(k, audio_f,
+                                          partial_results_cb=partial_cb,
+                                          partial_results_kwargs=partial_kwargs)
 
-    trans = standard_kaldi.transcribe(k, audio_f,
-                                      partial_results_cb=partial_cb,
-                                      partial_results_kwargs=partial_kwargs)
-
-    ret = diff_align.align(trans["words"], ms)
-
-    shutil.rmtree(gen_model_dir)
+        ret = diff_align.align(trans["words"], ms)
+    finally:
+        os.unlink(gen_hclg_filename)
 
     return {
         "transcript": transcript,
diff --git a/mkgraph.cc b/mkgraph.cc
index 413c894c..1b877186 100644
--- a/mkgraph.cc
+++ b/mkgraph.cc
@@ -13,11 +13,11 @@ int main(int argc, char *argv[]) {
 	using namespace fst;
 	using fst::script::ArcSort;
 	try {
-		const char *usage = "Usage: ./mkgraph [options] <lang-dir> <model-dir> <grammar-fst> <words-txt> <out-fst>\n";
+		const char *usage = "Usage: ./mkgraph [options] <proto-dir> <grammar-fst> <out-fst>\n";
 		
 		ParseOptions po(usage);
 		po.Read(argc, argv);
-		if (po.NumArgs() != 5) {
+		if (po.NumArgs() != 3) {
 			po.PrintUsage();
 			return 1;
 		}
@@ -27,17 +27,16 @@ int main(int argc, char *argv[]) {
 		float self_loop_scale = 0.1;
 		bool reverse = false;
 
-		std::string lang_dir = po.GetArg(1),
-					model_dir = po.GetArg(2),
-					grammar_fst_filename = po.GetArg(3),
-					words_filename = po.GetArg(4),
-					out_filename = po.GetArg(5);
-
-		std::string lang_fst_filename = lang_dir + "/L.fst",
-			lang_disambig_fst_filename = lang_dir + "/L_disambig.fst",
-			disambig_phones_filename = lang_dir + "/phones/disambig.int",
-			model_filename = model_dir + "/final.mdl",
-			tree_filename = model_dir + "/tree";
+		std::string proto_dir = po.GetArg(1),
+					grammar_fst_filename = po.GetArg(2),
+					out_filename = po.GetArg(3);
+
+		std::string lang_fst_filename = proto_dir + "/langdir/L.fst",
+			lang_disambig_fst_filename = proto_dir + "/langdir/L_disambig.fst",
+			disambig_phones_filename = proto_dir + "/langdir/phones/disambig.int",
+			model_filename = proto_dir + "/modeldir/final.mdl",
+			tree_filename = proto_dir + "/modeldir/tree",
+			words_filename = proto_dir + "/graphdir/words.txt";
 
 		if (!std::ifstream(lang_fst_filename.c_str())) {
 			std::cerr << "expected " << lang_fst_filename << " to exist" << std::endl;
diff --git a/tests/data/lucier_golden.json b/tests/data/lucier_golden.json
index dc6d782c..e6f02299 100644
--- a/tests/data/lucier_golden.json
+++ b/tests/data/lucier_golden.json
@@ -1,3162 +1,3165 @@
 {
-  "words": [
-    {
-      "case": "success", 
-      "start": 0.0, 
-      "word": "I", 
-      "startOffset": 0, 
-      "endOffset": 1, 
-      "phones": [
-        {
-          "duration": 0.07, 
-          "phone": "ay_S"
-        }
-      ], 
-      "end": 0.07, 
-      "alignedWord": "i"
-    }, 
-    {
-      "case": "success", 
-      "start": 0.11, 
-      "word": "am", 
-      "startOffset": 2, 
-      "endOffset": 4, 
-      "phones": [
-        {
-          "duration": 0.03, 
-          "phone": "ae_B"
-        }
-      ], 
-      "end": 0.14, 
-      "alignedWord": "am"
-    }, 
-    {
-      "case": "not-found-in-audio", 
-      "word": "sitting", 
-      "startOffset": 5, 
-      "endOffset": 12
-    }, 
-    {
-      "case": "not-found-in-audio", 
-      "word": "in", 
-      "startOffset": 13, 
-      "endOffset": 15
-    }, 
-    {
-      "case": "not-found-in-transcript", 
-      "phones": [
-        {
-          "duration": 0.03, 
-          "phone": "m_E"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "r_B"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "ah_I"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "k_I"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "ao_I"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "r_I"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "d_I"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "ih_I"
-        }, 
-        {
-          "duration": 0.42, 
-          "phone": "ng_E"
-        }
-      ], 
-      "end": 0.8, 
-      "start": 0.14, 
-      "alignedWord": "recording"
-    }, 
-    {
-      "case": "not-found-in-transcript", 
-      "phones": [
-        {
-          "duration": 0.05, 
-          "phone": "dh_B"
-        }, 
-        {
-          "duration": 0.06, 
-          "phone": "iy_E"
-        }
-      ], 
-      "end": 0.9400000000000001, 
-      "start": 0.8300000000000001, 
-      "alignedWord": "the"
-    }, 
-    {
-      "case": "not-found-in-transcript", 
-      "phones": [
-        {
-          "duration": 5.08, 
-          "phone": "s_B"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "aw_I"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "n_I"
-        }, 
-        {
-          "duration": 0.07, 
-          "phone": "d_E"
-        }
-      ], 
-      "end": 6.18, 
-      "start": 0.97, 
-      "alignedWord": "sound"
-    }, 
-    {
-      "case": "not-found-in-transcript", 
-      "phones": [
-        {
-          "duration": 0.03, 
-          "phone": "ah_B"
-        }, 
-        {
-          "duration": 0.06, 
-          "phone": "v_E"
-        }
-      ], 
-      "end": 6.35, 
-      "start": 6.26, 
-      "alignedWord": "of"
-    }, 
-    {
-      "case": "success", 
-      "start": 6.74, 
-      "word": "a", 
-      "startOffset": 16, 
-      "endOffset": 17, 
-      "phones": [
-        {
-          "duration": 0.12, 
-          "phone": "ah_S"
-        }
-      ], 
-      "end": 6.86, 
-      "alignedWord": "a"
-    }, 
-    {
-      "case": "not-found-in-transcript", 
-      "phones": [
-        {
-          "duration": 0.03, 
-          "phone": "ah_S"
-        }, 
-        {
-          "duration": 0.04, 
-          "phone": "d_B"
-        }, 
-        {
-          "duration": 0.13, 
-          "phone": "eh_I"
-        }, 
-        {
-          "duration": 0.04, 
-          "phone": "m_I"
-        }, 
-        {
-          "duration": 0.04, 
-          "phone": "ah_I"
-        }, 
-        {
-          "duration": 0.05, 
-          "phone": "n_I"
-        }, 
-        {
-          "duration": 0.08, 
-          "phone": "s_I"
-        }, 
-        {
-          "duration": 0.43, 
-          "phone": "t_I"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "r_I"
-        }, 
-        {
-          "duration": 0.04, 
-          "phone": "ey_I"
-        }, 
-        {
-          "duration": 0.06, 
-          "phone": "sh_I"
-        }, 
-        {
-          "duration": 0.07, 
-          "phone": "ah_I"
-        }
-      ], 
-      "end": 7.9, 
-      "start": 6.86, 
-      "alignedWord": "demonstration"
-    }, 
-    {
-      "case": "not-found-in-transcript", 
-      "phones": [
-        {
-          "duration": 0.09, 
-          "phone": "n_E"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "ah_B"
-        }
-      ], 
-      "end": 8.02, 
-      "start": 7.9, 
-      "alignedWord": "of"
-    }, 
-    {
-      "case": "not-found-in-transcript", 
-      "phones": [
-        {
-          "duration": 0.05, 
-          "phone": "v_E"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "dh_B"
-        }, 
-        {
-          "duration": 0.08, 
-          "phone": "iy_E"
-        }
-      ], 
-      "end": 8.18, 
-      "start": 8.02, 
-      "alignedWord": "the"
-    }, 
-    {
-      "case": "success", 
-      "start": 8.179999, 
-      "word": "room", 
-      "startOffset": 18, 
-      "endOffset": 22, 
-      "phones": [
-        {
-          "duration": 0.13, 
-          "phone": "r_B"
-        }, 
-        {
-          "duration": 0.12, 
-          "phone": "uw_I"
-        }, 
-        {
-          "duration": 0.35, 
-          "phone": "m_E"
-        }
-      ], 
-      "end": 8.779999, 
-      "alignedWord": "room"
-    }, 
-    {
-      "case": "success", 
-      "start": 11.639999, 
-      "word": "different", 
-      "startOffset": 23, 
-      "endOffset": 32, 
-      "phones": [
-        {
-          "duration": 0.09, 
-          "phone": "d_B"
-        }, 
-        {
-          "duration": 0.09, 
-          "phone": "ih_I"
-        }, 
-        {
-          "duration": 0.14, 
-          "phone": "f_I"
-        }, 
-        {
-          "duration": 0.04, 
-          "phone": "r_I"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "ah_I"
-        }, 
-        {
-          "duration": 0.1, 
-          "phone": "n_I"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "t_E"
-        }
-      ], 
-      "end": 12.159999, 
-      "alignedWord": "different"
-    }, 
-    {
-      "case": "success", 
-      "start": 12.19, 
-      "word": "from", 
-      "startOffset": 33, 
-      "endOffset": 37, 
-      "phones": [
-        {
-          "duration": 0.15, 
-          "phone": "f_B"
-        }, 
-        {
-          "duration": 0.03, 
-          "phone": "r_I"
-        }, 
-        {
-          "duration": 0.04, 
-          "phone": "ah_I"
-        }
-      ], 
-      "end": 12.41, 
-      "alignedWord": "from"
-    }, 
-    {
-      "case": "success", 
-      "start": 12.41, 
-      "word": "the", 
-      "startOffset": 38, 
-      "endOffset": 41, 
-      "phones": [
-        {
-          "duration": 0.21, 
-          "phone": "m_E"
-        }, 
-        {
-          "duration": 0.04, 
-          "phone": "dh_B"
-        }
-      ], 
-      "end": 12.66, 
-      "alignedWord": "the"
-    }, 
-    {
-      "case": "success", 
-      "start": 12.66, 
-      "word": "one", 
-      "startOffset": 42, 
-      "endOffset": 45, 
-      "phones": [
-        {
-          "duration": 0.07, 
-          "phone": "iy_E"
-        }, 
-        {
-          "duration": 0.08, 
-          "phone": "w_B"
-        }, 
-        {
-          "duration": 0.1, 
-          "phone": "ah_I"
-        }
-      ], 
-      "end": 12.91, 
-      "alignedWord": "one"
-    }, 
-    {
-      "case": "success", 
-      "start": 12.91, 
-      "word": "you", 
-      "startOffset": 46, 
-      "endOffset": 49, 
-      "phones": [
-        {
-          "duration": 0.09, 
-          "phone": "n_E"
-        }, 
-        {
-          "duration": 0.07, 
-          "phone": "y_B"
-        }
-      ], 
-      "end": 13.07, 
-      "alignedWord": "you"
-    }, 
-    {
-      "case": "success", 
-      "start": 13.07, 
-      "word": "are", 
-      "startOffset": 50, 
-      "endOffset": 53, 
-      "phones": [
-        {
-          "duration": 0.21, 
-          "phone": "uw_E"
-        }, 
-        {
-          "duration": 0.09, 
-          "phone": "aa_B"
-        }
-      ], 
-      "end": 13.370000000000001, 
-      "alignedWord": "are"
-    }, 
-    {
-      "case": "success", 
-      "start": 13.37, 
-      "word": "in", 
-      "startOffset": 54, 
-      "endOffset": 56, 
-      "phones": [
-        {
-          "duration": 0.06, 
-          "phone": "r_E"
-        }, 
-        {
-          "duration": 0.1, 
-          "phone": "ih_B"
-        }
-      ], 
-      "end": 13.53, 
-      "alignedWord": "in"
-    }, 
-    {
-      "case": "success", 
-      "start": 13.53, 
-      "word": "now", 
-      "startOffset": 57, 
-      "endOffset": 60, 
-      "phones": [
-        {
-          "duration": 0.15, 
-          "phone": "n_E"
-        }, 
-        {
-          "duration": 0.08, 
-          "phone": "n_B"
-        }, 
-        {
-          "duration": 0.39, 
-          "phone": "aw_E"
-        }
-      ], 
-      "end": 14.149999999999999, 
-      "alignedWord": "now"
-    }, 
-    {
-      "case": "success", 
-      "start": 17.77, 
-      "word": "I", 
-      "startOffset": 62, 
-      "endOffset": 63, 
-      "phones": [
-        {
-          "duration": 0.08, 
-          "phone": "ay_S"
-        }
-      ], 
-      "end": 17.85, 
-      "alignedWord": "i"
-    }, 
-    {
-      "case": "success", 
-      "start": 17.85, 
-      "word": "am", 
-      "startOffset": 64, 
-      "endOffset": 66, 
-      "phones": [
-        {
-          "duration": 0.05, 
-          "phone": "ay_S"
-        }, 
-        {
-          "duration": 0.06, 
-          "phone": "ey_B"
-        }, 
-        {
-          "duration": 0.04, 
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 }
\ No newline at end of file